This review discusses the application of machine learning (ML) in predicting the photocatalytic degradation of drugs and dyes, which is crucial for efficient wastewater treatment. The increasing amount of organic pollutants in wastewater necessitates accurate predictive models to optimize treatment processes. ML techniques, such as artificial neural networks, support vector machines, decision trees, and genetic algorithms, have significantly improved the accuracy of these models. These models help in designing and optimizing wastewater treatment processes, leading to more effective removal of harmful contaminants. The review highlights the challenges in traditional degradation methods and introduces ML as a promising solution for enhancing the efficiency, accuracy, and predictability of degradation processes. It discusses the importance of factors such as pH, catalyst concentration, light irradiation, temperature, and the nature of the catalyst in photocatalytic degradation. The integration of ML with photocatalytic processes offers benefits such as improved efficiency, reduced energy consumption, and better decision-making in waste treatment. The review also explores the application of ML in predicting the catalytic degradation performance of organic pollutants, including the use of various ML algorithms like ANN, gradient boosting regression, and random forest regression. These models can accurately predict the degradation efficiency of pollutants and help in selecting the most effective treatment technologies. The study emphasizes the potential of ML in identifying the most influential factors in degradation processes and optimizing catalyst design. The review covers various applications of ML in pollution control, including predicting the effectiveness of different catalysts, optimizing reaction conditions, identifying key factors affecting degradation, assessing environmental impact, designing tailored treatment plans, and developing real-time monitoring systems. These applications demonstrate the potential of ML in improving the efficiency and sustainability of wastewater treatment processes. The integration of ML with photocatalytic degradation offers a promising avenue for addressing the challenges of removing organic pollutants from wastewater.This review discusses the application of machine learning (ML) in predicting the photocatalytic degradation of drugs and dyes, which is crucial for efficient wastewater treatment. The increasing amount of organic pollutants in wastewater necessitates accurate predictive models to optimize treatment processes. ML techniques, such as artificial neural networks, support vector machines, decision trees, and genetic algorithms, have significantly improved the accuracy of these models. These models help in designing and optimizing wastewater treatment processes, leading to more effective removal of harmful contaminants. The review highlights the challenges in traditional degradation methods and introduces ML as a promising solution for enhancing the efficiency, accuracy, and predictability of degradation processes. It discusses the importance of factors such as pH, catalyst concentration, light irradiation, temperature, and the nature of the catalyst in photocatalytic degradation. The integration of ML with photocatalytic processes offers benefits such as improved efficiency, reduced energy consumption, and better decision-making in waste treatment. The review also explores the application of ML in predicting the catalytic degradation performance of organic pollutants, including the use of various ML algorithms like ANN, gradient boosting regression, and random forest regression. These models can accurately predict the degradation efficiency of pollutants and help in selecting the most effective treatment technologies. The study emphasizes the potential of ML in identifying the most influential factors in degradation processes and optimizing catalyst design. The review covers various applications of ML in pollution control, including predicting the effectiveness of different catalysts, optimizing reaction conditions, identifying key factors affecting degradation, assessing environmental impact, designing tailored treatment plans, and developing real-time monitoring systems. These applications demonstrate the potential of ML in improving the efficiency and sustainability of wastewater treatment processes. The integration of ML with photocatalytic degradation offers a promising avenue for addressing the challenges of removing organic pollutants from wastewater.