Applied Chemical Engineering

Abstract The management of marine dredged sediments is a critical environmental and economic issue, particularly in port cities where dredging is a necessary activity to maintain navigability. These sediments are typically viewed as waste products and often require costly and environmentally challenging disposal methods. However, repurposing dredged sediments as a component in concrete production presents a promising solution for both waste management and the creation of sustainable construction materials. Despite this potential, determining the optimal percentage of sediment incorporation and accurately predicting the mechanical properties, such as compressive strength, remain significant challenges. This study proposes an artificial intelligence (AI)-based approach to predict the optimal incorporation percentage of marine dredged sediments from Moroccan ports into concrete and to forecast the resulting compressive strength. A dataset consisting of 104 samples, including dune sand and port sediments from JEBHA, was used. The data includes key properties such as granulometry, cleanliness, fineness modulus, and the compressive strength of the concrete mixtures. These experimental data were employed to train and validate several machine learning models, including linear regression, Random Forest, Gradient Boosting, and XGBoost, chosen for their ability to model complex, non-linear relationships between sediment characteristics and concrete performance. The performance of these models was evaluated using two key metrics: the coefficient of determination (R²) and the root mean square error (RMSE). Among the models tested, the Random Forest Regressor delivered the best results, with an R² value greater than 0.98 and an RMSE of less than 0.20 MPa, indicating highly accurate predictions of both the optimal sediment incorporation rate and the compressive strength of the concrete. This model’s exceptional performance underscores its potential as a reliable tool for optimizing the use of dredged sediments in concrete production. The findings of this study demonstrate the considerable potential of AI in optimizing the incorporation of marine dredged sediments into concrete. By accurately predicting the mechanical properties of the resulting material, this approach enables the development of more sustainable construction practices while reducing the environmental burden associated with sediment disposal. Moreover, this work illustrates the broader applicability of AI in addressing environmental challenges, offering a pathway to valorize waste materials in the construction industry. The study not only advances our understanding of sediment utilization in concrete but also contributes to the growing field of sustainable material science, offering promising avenues for future research and development. Nevertheless, further research is needed to validate the model’s scalability to other sediment types and assess practical limitations in industrial applications.

Abstract This paper describes the preparation of g-C 3 N 4  by polycondensation of melamine at 511 °C and its subsequent doping with Fe 2 O 3 , Cu, GO, rGO, DA and their combinations. Graphitic carbon nitride represents an innovative material with numerous applications, particularly within the domains of catalysis and water treatment. FTIR and thermogravimetric analysis were predominantly employed for the purpose of identification, while SEM images were captured at incremental resolutions. The subsequent section of the manuscript delineates the fabrication of composite materials within polymers, including PE-foil and PP-filaments intended for 3D printing, as well as PVB into nanofibers via electrostatic spinning techniques. The objective of this study was to examine the thermal stability of nanofibers produced from PVB in conjunction with g-C3N4 composite (18-20 %) utilizing two methodologies: EL DC spinning and EL AC spinning. The material exhibiting the most efficacious synthesis will undergo further assessment to evaluate its potential utility in the photocatalytic degradation of environmental pollutants.

Abstract Proper control of the droplet impact mechanism is very crucial in enhancing efficient use of resources in a variety of industrial practices such as spray cooling, anti-icing systems, and surface coating processes. To determine how droplet impingement occurred over time, the current study focuses on the investigation of such a process on micro- and micro/nanotextured and lubricant-infused surfaces (LIS). The visualization of impact events at different Weber numbers ( We  = 2~200) was carried out by means of high-speed imaging. Microelectromechanical systems (MEMS) fabrication techniques were applied to realize precise surface engineering. The findings show a significant increase in the droplet deposition and the accompanying inhibition of splashing, which directly provides a benefit on resources utilization and process stability. When connecting results to SDG 9.4 that suggests upgrading the existing industrial infrastructure by aiming to achieve the high degree of resource-use efficiency, the given study emphasizes that the most breakthrough surface engineering mechanisms hold great potential to transform sustainable industrial processes.

Abstract The increase in global energy consumption is paralleled by an increase in the waste generated from it, especially those related to used batteries which constitute a source of contamination of the environment and a great attaint for the human health. Therefore, it has become more necessary to work on recycling batteries to revalue their active materials and to preserve the environment. The aim of this work was the synthesis and characterization of nanostructured PbO obtained from spent lead acid batteries negative plate. The negative plates of used battery are made up of large amounts of PbSO4 and smaller amounts of Pb. The PbSO 4  was desulfated with (NH 4 ) 2 CO 3  to obtain PbCO 3  which is then calcined in air at different temperatures.in this work we are interested in studying the effect of temperature on the nature and the morphology of the products of the calcination process. The results show that at a 450°c we obtain α-PbO, at 500°c β-PbO, after these temperature we get a mixture of lead oxides α-PbO, β-PbO, and minium Pb 3 O 4 . α-PbO granules have sizes around 26 nm with a mesoporous materials and BET surface area was equal to about 4 m 2 /g.

Abstract Recently, water pollution with dyes is one of the most serious problems and the most dangerous to human health and living organisms. This study involves the synthesis of magnetic nano form of Fe-Mn binary oxide modified biomass derived of agricultural waste commercial (wood shavings) as a raw material. Magnetic nanoparticles were prepared from activated carbon obtained from lignocellulose through chemical activation of wood shavings using NaOH incorporated with (FeCl3.6H2O), ferrous sulphate heptahydrate (FeSO4.7H2O) and potassium permanganate (KMnO4) for effective removal of MO dye from aqueous solutions in a batch processes. This material was characterized through several advanced techniques such as Fourier transform infrared Spectroscopy (FTIR), X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Thermo gravimetric analysis (TGA), Energy -dispersive X-ray spectroscopy (EDS), Transmission electron Microscopy (TEM) and Vibration sample magnetometer (VSM). These analysis techniques highlighted the successful synthesis of magnetic nanocomposite with a porous structure. Batch adsorption experiments were studied by including contact time, adsorbent dose, pH, and temperature to determine the optimal conditions for maximum dye removal efficiency. The developed LB-Fe/Mn nanocomposite demonstrated excellent removal efficiency (98.16%) for methyl orange, outperforming many current advanced materials. Thermodynamic study revealed the endothermic and spontaneous nature of studied process. Adsorption kinetics followed a pseudo-second-order model (R² = 0.9676), while Freundlich (R² = 0.9544) and Temkin (R² = 0.9545) isotherms best described the equilibrium data, indicating multilayer adsorption on heterogeneous surfaces with abundant binding sites.

Abstract The present study focused on the phytochemical composition of the brown seaweed Hormophysa cuneiformis  and its anticancer activity in A549 lung cancer cells. Ethanolic and aqueous extracts were prepared and analyzed for proximate analysis and phytochemical screening, followed by evaluation of their antiproliferative effects on A549 cells using the MTT assay. The results indicated that Hormophysa cuneiformis  extract had a highly significant ash content of 23.4 ± 2.4%  and various bioactive compounds, including alkaloids, flavonoids, and terpenoids. The ethanolic extract demonstrated better antiproliferative activity than the aqueous extract, with an IC50 value of 78.475 ± 1.723 μg/mL. Treatment with 200 μg/mL ethanolic extract inhibited cancer cell growth by > 50%. These changes include shrinkage and a reduction in the cell population. The ethanolic extract showed 90.46% DPPH radical scavenging activity at 50 mg/mL, and an H2O2 scavenging effect of 86.7% at the same concentration. These results support the potential anticancer and antioxidant activities of H. cuneiformis  and support further studies to explore therapeutic possibilities against lung cancer.

Abstract A highly sensitive approach for separating and determining the micro amount of nickel (II) was conducted. It has been achieved after the formation of chelation complexes with 4-((4-hydroxyquinolin-3-yl) diazenyl) benzenesulfonamide (HQDBS) and 3-((1H-indol-5-yl)diazenyl)quinolin-4-ol (IDQ) as complexing agents (which are examined by using UV-Vis., FT-IR, and 1 HNMR spectrum), including joint cloud point extraction with liquid ion exchange methods in the presence of the nonionic surfactant Triton X–100. The study is based on the wavelength values of maximum absorbance, λ max  = 480 and 484 nm, respectively. The study optimized the extraction conditions, including the reagent concentration, temperature, heating duration, and surfactant volume. The concentration of reagents for achieving higher extraction efficiency is 1×10 -3  M in the presence of 100 µg Ni (II)/mL of aqueous solution. The solutions should be heated at 80°C and 90°C HQDBS and IDQ respectively, for 15 minutes. The optimal volume of surfactants is 0.8 mL of Triton X-100 with HQDBS and 0.5 mL with IDQ. The study also includes an analysis of the impact of electrolytes and interferences and the spectrophotometric identification of Ni (II) in various samples.  

Abstract Graphene Nanoflakes (GNF) has used widely in the nanoelectronic and materials science field due to due to their mechanical and physical properties. The combination of the Density Functional Theory (DFT) computational method with the B3LYP functional and the 6-31G basis set, executed via the Gaussian 09 program, was used in this study to investigate the nanosensor's role in detecting the explosive organic molecule (RDX). This was achieved by determining the change in the band gap energy of the sensor, which influences its conductivity. The graphene sensor was modified by the addition of a substitutional palladium (Pd) atom. The results demonstrate an enhancement in device performance, evidenced by the change in the energy gap for all molecules studied. The better addition for the RDX sensor was found Pd atom due to improved electronic properties such as optimized structure, dipole moment, a decrease in the HOMO-LUMO energy gap, total energy, and density of states.

Abstract In the modern era, there has been a growing focus among researchers on the transition from fossil fuels to renewable energy sources, particularly photovoltaic (PV) energy, which is gaining popularity worldwide. As the development and installation of PV systems accelerate globally, it is essential to address the various faults and failures these systems may encounter. Consequently, fault diagnosis and evaluation have emerged as critical areas of study aimed at enhancing performance, improving system efficiency, and reducing maintenance costs and repair times. This paper proposes the use of a Random Forest classifier (RF) for diagnosing short circuit and open circuit faults in PV systems. The classifier is trained using machine learning algorithms to accurately identify different fault types based on real measured data from an experimental PV setup. This data encompasses weather conditions such as cell temperature and solar irradiation, as well as system parameters like current and voltage at the maximum power point, alongside performance metrics. The Random Forest classifier serves as a proactive tool for maintenance and fault diagnosis in PV systems, contributing to better overall performance and reliability. Testing on real-world data from a PV system demonstrates that this approach achieves remarkable accuracy in fault diagnosis, with a precision of 100% for current classification and around 97% for voltage classification, all within a few seconds for each parameter.

Abstract This article explores the mathematical framework and computational implementation of the “Law of Mass Action” to model the kinetics of chemical reaction. The study begins with a detailed explanation of the governing equations, emphasizing the role of stoichiometry and reaction orders in dynamic systems. Using Python, a generalized computational framework was developed to solve systems of ordinary differential equations (ODEs) that describe concentration changes over time. The function solve_ivp has been used from the SciPy module to perform the task of solving ODEs. The solver is capable of handling complex reaction networks by incorporating a stoichiometric matrix, reaction rate constants, and reaction orders as inputs. The results are plotted and tabulated with the help of Matplotlib.pylab and Pandas modules. Two representative examples, including real-world chemical reactions, were solved to demonstrate the versatility and accuracy of the approach. Results show that this generalized methodology provides an efficient and adaptable tool for chemical reaction modelling. This work highlights the power of combining mathematics with modern programming to solve practical chemical engineering problems.

Abstract Borophosphate glasses with compositions xNa 2 O-(45-x) B 2 O 3 -45P 2 O 5 -10MnO, where x ranges from 5 to 25 mol%, have been prepared using the conventional melt quenching technique. Several methods including X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) have been used to characterize the produced materials. The absence of crystalline structure in the prepared phosphate glasses was confirmed by X-ray diffraction (XRD) studies. The chemical resistance of these glasses increases with the Na 2 O content. Glasses containing more than 15 mol% Na 2 O have excellent chemical resistance. The relationship between structural changes and composition was investigated by measuring density and glass transition temperature Tg. The results obtained show that the glass transition temperature and chemical properties increase with increasing sodium oxide composition in all the glasses studied. These experimental results indicate that Na 2 O lowers the melting point and increases the strength of the glasses.

Abstract Syntactic foam composites are used in the aerospace industry, one of which is for buoys because they are lightweight materials that have flexural strength and are resistant to corrosion. This study aims to determine the flexural properties and morphologies structure of syntactic foam composite with K-15 microballoons filler mixed by epoxy resin. Syntactic foam is a lightweight material used in the maritime sector for buoys to make them float easily in water. Variations of K-15 microballoons of 10%, 20%, 30%, 40% and 50% volume of syntactic foam composite. Flexural strength determinated using ASTM D790 and morphologie structure determined with scanning electron microscope (SEM). The addition of X-15 microballoons can reduce flexural strength compared to pure epoxy. Flexural strength in pure epoxy in the form of stress, elastic modulus, and strain each produces values of 71.28, 2928.47 MPa, 2.75 MPa. The highest impact is seen in the addition of 50% K-15 microballoons with stress, elastic modulus, strain and density values of 18.59, 1406.03 MPa, 1.43 MPa and 0.63 kgm-3 respectively. SEM analysis shows that plain epoxy has a smooth surface without voids, whereas with the addition of K-15 microballoons, voids appear on the surface of the syntactic foam composite.

Abstract Fused Deposition Modeling (FDM)-based rapid prototyping is a key technology in sustainable manufacturing, offering cost-effective solutions aligned with the United Nations Sustainable Development Goals (SDGs 1–6) by promoting affordable production, resource efficiency, and environmental sustainability. However, optimizing mechanical performance and energy efficiency in bio-based thermoplastic composites remains a challenge. This study explores PLA–walnut wood fiber composites, leveraging machine learning (ML) to optimize tensile, compression, and flexural properties while minimizing energy consumption. A dataset incorporating nozzle temperature, layer height, infill density, and print speed was trained using ML, achieving prediction accuracy above 95%. State-of-the-art studies highlight bio-based composite advantages, yet ML-driven multi-objective optimization for mechanical strength and sustainability remains unexplored. Experimental results indicate that an optimal nozzle temperature of 200–210°C, an infill density of 60–80%, and a layer height of 0.2 mm led to a 15% increase in tensile strength (38 MPa), a 12% improvement in flexural strength (62 MPa), and a 10% enhancement in compression strength (49 MPa). SEM analysis confirms improved fiber-matrix adhesion, enhancing structural integrity. Additionally, energy consumption was reduced by 18%, supporting cost-effective and low-carbon production. These findings contribute to poverty reduction (SDG 1), agricultural waste valorization (SDG 2), biocompatible materials for healthcare (SDG 3), STEM education accessibility (SDG 4), gender inclusivity in engineering (SDG 5), and clean water protection through reduced plastic waste (SDG 6). This study underscores the potential of ML-driven sustainable rapid prototyping to advance material efficiency, waste reduction, and resource-conscious manufacturing.

Abstract Nanofluids with advanced properties (also known as hybrid nanofluids), which form systems based on a combination of two or more nanoparticles (NPs dispersed in ordinary fluids), have been considered as a revolutionary solution to improve thermal efficiency in a wide range of industrial processes, and ultimately contribute to energy efficiency. These materials exhibit enhanced performance characteristics compared to traditional single-phase nanomaterials, owing to synergistic interactions between their components. Synthesis techniques such as sol-gel processing, hydrothermal methods, and chemical vapor deposition are extensively explored, each offering specific advantages in terms of particle dispersion, morphology control, and structural stability. The current critical review examines important synthesis techniques, delineating characteristics, and real-existing applications of the hybrid nanofluids and the ability of mixing them to increase the thermal conductivity and, subsequently, enhance energy conservation in heat exchangers and cooling devices. New trends confirm that the process of optimizing nanofluid arrangements can directly lead to Sustainable Development Goal (SDG) 7.3 by enhancing energy efficiency globally. At the same time, it critically evaluates the existing barriers such as stability, cost-effectiveness, and environmental friendliness as well as outline possible avenues of research that might raise the possibility of achievable industrial scalability and sustainability. Meanwhile, nanocomposites  show promise in photocatalysis and solar energy applications due to their superior light absorption and charge transport properties. Carbon-based hybrids exhibit outstanding electrical conductivity and are being developed for use in super capacitors, batteries, and electronic cooling systems. Biomedical applications, environmental sensors, and advanced fluid technologies (e.g., nanofluids) also benefit from these materials. Emphasis is placed on continued interdisciplinary research and innovation to unlock their full potential and expand their industrial applicability in the future. With strategic development, these materials promise transformative impacts across energy, medicine, electronics, and environmental sectors. To explore recent advancements in the synthesis techniques of hybrid nanofluids and analyze the thermophysical and chemical properties of hybrid nanofluids.

Abstract The aim of this article is to develop a "switchable hydrophilicity solvent liquid phase microextraction" (SHS-LPME) for the effectual extraction of fast green FCF. Three "switchable hydrophilicity solvents" (SHSs) were practiced for the extraction of fast green FCF. The attained extract phase afterward phase separation was evaluated by UV-VIS spectrophotometry. The extraction parameters such as, SHS volume, HNO3 volume and NaOH volume, were enhanced using central composite design and desirability function. Under optimized conditions, the linear range 0.50-5.00 µg/ mL with R2 = 0.9886, limit of detection 0.341 µg/ mL, limit of quantitation 1.026 µg/ mL. The method showed a relative standard deviation (RSD) of 1.16% for 7 replicate measurements. Preconcentration and Enrichment factors were determined to be 20 and 35 respectively, indicating the method’s efficiency in enhancing fast green detection. The proposed method was applied in real samples successfully.

Abstract The research aimed to develop a new deep eutectic solvent (DES) composed of a mixture of lithium chloride and 1,3-propanediol, mixed at a 1:4 molar ratio, and used it for testing of electroplating of copper. Structural and electrochemical properties of copper electrolytes were determined with and without the use of the additives by using various electrochemical techniques, including cyclic voltammetric (CVs) measurements. Physical properties of the additive-free and additive-dispensed propanediol-based DES, such as electrical conductivity, were determined. The intent of this analysis was to determine how the additives present in the solvent altered the dissolving properties of the solvent, thus changing the electroplating process. The research further extends the knowledge of the newly introduced DES with respect to its impact on copper electrodeposition on nickel substrates. This evaluation was undertaken both with and without additives in order to assess their impact on the electroplating process. Important properties of the deposited copper, including thickness, surface shape, and surface roughness, were measured and compared. Finally, more sophisticated methods have been used to gain a deeper understanding of the copper deposits. The surface morphology of the deposits was analyzed by field emission-scanning electron microscopy (FESEM), and atomic force microscopy (AFM) was particularly useful in ascertaining the surface roughness. These methods enabled the observation and measurement of the microstructural variances of the copper deposits produced in different experimental setups.

Abstract Herein, a new imine-oxime ligand was synthesized in a two-step reaction. In the  first step, 2-aminothiazole and benzil were reacted with glacial acetic acid as a catalyst resulting in the formation of 1,2-diphenyl-2-(thiazol-2-ylimino)ethan-1-one. In the second step, this intermediate was then reacted with diacetyl monoxime and 4,4'-methylenedianiline for synthesis of target ligand, PTIEABPBO. Afterwards, a palladium (II) complex of this ligand was also synthesized. Analyses of ligand and its complex were carried out via FT-IR, UV-Vis, and ¹H, ¹³C-NMR, analysis of melting point, elemental analysis, FESEM, and XRD. Findings of morphological study confirmed the purity of the synthesized compounds by demonstrating close agreement between calculated and experimental values. FT-IR highlights presence of azomethine and oxime bands in ligand, with shifts observed upon complexation with palladium. Crystallographic study indicated a crystalline, nanoscale structure for both compounds. Finally, biological evaluation demonstrated significant inhibitory activity of synthesized compounds against MCF-7 breast cancer cell line, when compared against HEK-293 normal cell line.

Abstract The central nervous system (CNS) is one of the primary targets of alcohol-induced damage. Chronic alcohol consumption leads to cognitive deficits, motor impairments, anxiety-like behaviors, and even irreversible neuronal degeneration and death. However, therapeutic strategies for alcohol-related neurotoxicity remain limited, posing a significant public health concern. Ursolic acid (UA), with its antioxidant, anticancer, anti-inflammatory, hepatoprotective, and immunomodulatory properties, may confer protective effects against neurological damage. In this study, we established a zebrafish model of alcohol-induced neurotoxicity and investigated the potential of UA to mitigate neural injury. Using confocal live imaging in transgenic zebrafish lines, we observed that UA significantly alleviated alcohol-induced reductions in neuronal and dopaminergic neuron populations. Behavioral assays further demonstrated that UA restored normal locomotor activity in zebrafish embryos, indicating functional recovery of the nervous system. Transcriptomic sequencing revealed that UA ameliorated alcohol-induced neurotoxicity potentially by modulating the MAPK signaling pathway and promoting extracellular matrix (ECM) remodeling. This study provides experimental evidence for UA as a therapeutic candidate against alcohol-related neural damage and identifies potential molecular targets for clinical interventions.

Abstract Heterocyclic compounds, characterized by rings containing non-carbon atoms like nitrogen, oxygen, or sulfur, are fundamental in diverse fields. This review provides an overview of heterocyclic chemistry, with a focused examination of benzimidazoles. It covers their structure, chemical properties, synthetic methods (including classical and modern techniques emphasizing efficiency and sustainability), and broad therapeutic applications across various disease areas, highlighting their significance in drug development and materials science. From (2018-2024)

Abstract Background:  Coumarins, a class of naturally occurring α-benzopyrones, have attracted substantial interest due to their diverse structural versatility and wide range of industrial and biomedical applications. Their photophysical properties, reactive moieties, and ease of functionalization position them as valuable agents in applied chemical engineering. Aim: This review aims to comprehensively examine the role of coumarins in applied chemical engineering, highlighting their transition from natural plant-derived scaffolds to synthetic molecules with advanced functionalities for industrial and pharmaceutical use. Methods:  The article compiles and analyzes current literature on the sources, biosynthesis, and synthetic strategies for coumarins and their derivatives. It explores their physicochemical properties, functionalization methods, and implementation in diverse applications, including material science, catalysis, drug development, and environmental remediation. Results: Coumarins exhibit significant promise in various domains due to their inherent photoreactivity, electronic delocalization, and biological compatibility. Engineered coumarin-based materials have demonstrated practical utility in bioimaging, smart coatings, sensors, and therapeutic agents. The review also discusses eco-friendly synthesis techniques, recent advances in structure-activity relationships, and challenges associated with scalability and toxicity. Conclusion:  Coumarins represent a crucial intersection between natural product chemistry and modern engineering. Their multifunctionality enables them to serve as adaptable platforms for the design of next-generation materials and therapeutics. Future work should prioritize sustainable production methods, industrial scalability, and enhanced biocompatibility to unlock their full potential in applied chemical engineering.

Abstract Coumarins, a class of benzopyrone derivatives predominantly found in plants, have garnered extensive scientific interest for their broad-spectrum biological activities and promising applications across pharmaceutical, agricultural, and cosmetic sectors. Their historical use in traditional medicine, combined with modern evidence supporting their therapeutic potential, positions coumarins as valuable natural scaffolds for drug development and sustainability-driven innovation. This review explores the natural diversity, biosynthesis, biological activities, and sustainable development strategies associated with coumarins. Emphasis is placed on their role in modern pharmacology, the advances in synthetic biology, and their applications within the context of environmental conservation and green chemistry. A comprehensive analysis was conducted using peer-reviewed literature obtained from major databases including PubMed, Scopus, and Web of Science. Key topics include coumarin biosynthesis, plant and microbial sources, traditional and modern applications, and sustainability practices related to their extraction and commercialization. Coumarins demonstrate potent antimicrobial, antioxidant, anti-inflammatory, and anticancer properties, many of which are linked to structural variations in their core scaffold. Advances in metabolic engineering and synthetic biology have enabled scalable production and derivatization. Coumarin-based compounds are increasingly being applied in skincare formulations, eco-friendly agrochemicals, and as templates in drug discovery. Ethical sourcing, conservation strategies, and regulatory frameworks play critical roles in ensuring sustainable utilization. Coumarins exemplify the convergence of natural product chemistry and sustainable innovation. Their structural diversity, bioactivity, and multifaceted applications underscore their importance in both traditional and modern contexts. Future research should focus on biosynthetic optimization, novel therapeutic targeting, and integration into circular bioeconomy frameworks to maximize their scientific and societal impact.

Abstract Coumarins, a diverse class of benzopyrone derivatives, have long captivated researchers due to their broad spectrum of pharmacological activities and synthetic versatility. In recent years, the convergence of artificial intelligence (AI) with pharmaceutical sciences has redefined how researchers approach the synthesis, molecular docking, and pharmacological profiling of such bioactive compounds. This review explores the transformative potential of AI in the context of coumarin research, presenting a holistic view of how machine learning algorithms, deep learning models, and data-driven design strategies are reshaping drug discovery. Traditional synthesis of coumarins, often constrained by multistep protocols and environmental concerns, is now being revolutionized through AI-assisted reaction predictions and retrosynthetic analyses. AI enables the generation of synthetically accessible molecules with optimized structural features, significantly reducing time and resource investment. Furthermore, molecular docking, critical to understanding structure-activity relationships, is increasingly benefiting from AI-enhanced scoring functions and predictive modeling, thus improving the accuracy of ligand-receptor interaction predictions. Pharmacological profiling, both in vitro and in vivo, is becoming more streamlined with AI models capable of predicting bioactivity, toxicity, and pharmacokinetics, making the lead optimization process more efficient and reliable. Public databases, curated datasets, and integrative cheminformatics platforms now provide a rich foundation for data mining and drug-target interaction studies. This review not only highlights the successes of AI in coumarin-based drug design but also discusses existing challenges, including algorithm interpretability, data quality, and regulatory considerations. Ultimately, the synergy between AI and coumarin research presents an exciting frontier that holds immense promise for accelerating drug discovery and advancing personalized therapeutics.