The application of Artificial Intelligence (AI) and Machine Learning (ML) in river water quality monitoring has revolutionized environmental management. AI/ML techniques enable the analysis of complex, nonlinear relationships between various water quality parameters, such as dissolved oxygen, biological and chemical oxygen demand, turbidity, ion concentrations and pollutant levels. These technologies facilitate real-time monitoring, predictive analytics and early detection of anomalies, thereby enhancing the accuracy and efficiency of water quality assessments. Supervised learning algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF) and Decision Trees (DT), are commonly employed for these tasks. Additionally, hybrid models combining multiple algorithms have shown promise in improving prediction accuracy. AI/ML models can integrate data from diverse sources, such as sensors and satellite imagery, to provide comprehensive assessments of water conditions. The implementation of AI/ML in river water quality monitoring supports the development of adaptive strategies for water resource management, contributing to sustainable environmental practices. However, challenges remain, including the need for high-quality data and the generalization of models to different geographical and climatic conditions. Continued research and innovation in this field are essential for advancing the capabilities and applications of AI/ML in environmental monitoring.