The chapter discusses the application of machine learning (ML) in predicting and optimizing the photocatalytic degradation of drugs and dyes, which are common pollutants in wastewater. The increasing volume of waste generated necessitates accurate models to forecast the behavior of organic pollutants during catalytic degradation. ML techniques have significantly improved these models, making them crucial for enhancing wastewater treatment strategies. The chapter highlights the challenges faced by traditional methods in ensuring water quality and emphasizes the importance of modern strategies. Photocatalysis, particularly using nanomaterials like titanium dioxide (TiO2), is a promising technique for removing organic pollutants. However, it faces limitations such as low efficiency under visible light and the formation of toxic byproducts. The integration of ML algorithms with photocatalysis offers a sustainable solution by providing accurate predictions and optimizing reaction conditions. The chapter also reviews the key factors influencing photocatalytic degradation, including pH, dye concentration, catalyst concentration, light irradiation, temperature, and catalyst nature. ML models are used to analyze these factors and predict degradation efficiency, with various techniques such as artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) being employed. The effectiveness of ML algorithms in forecasting catalytic degradation performance is demonstrated through several studies, showing high accuracy and reliability. The chapter concludes by discussing the applications of ML in pollution control, including drug and dye degradation, and the potential of ML to revolutionize the field of catalytic degradation.The chapter discusses the application of machine learning (ML) in predicting and optimizing the photocatalytic degradation of drugs and dyes, which are common pollutants in wastewater. The increasing volume of waste generated necessitates accurate models to forecast the behavior of organic pollutants during catalytic degradation. ML techniques have significantly improved these models, making them crucial for enhancing wastewater treatment strategies. The chapter highlights the challenges faced by traditional methods in ensuring water quality and emphasizes the importance of modern strategies. Photocatalysis, particularly using nanomaterials like titanium dioxide (TiO2), is a promising technique for removing organic pollutants. However, it faces limitations such as low efficiency under visible light and the formation of toxic byproducts. The integration of ML algorithms with photocatalysis offers a sustainable solution by providing accurate predictions and optimizing reaction conditions. The chapter also reviews the key factors influencing photocatalytic degradation, including pH, dye concentration, catalyst concentration, light irradiation, temperature, and catalyst nature. ML models are used to analyze these factors and predict degradation efficiency, with various techniques such as artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) being employed. The effectiveness of ML algorithms in forecasting catalytic degradation performance is demonstrated through several studies, showing high accuracy and reliability. The chapter concludes by discussing the applications of ML in pollution control, including drug and dye degradation, and the potential of ML to revolutionize the field of catalytic degradation.