Machine learning (ML), which has recently gained popularity and is crucial to computer science, artificial intelligence, chemistry, and biology, has captured people’s attention. The chapter focuses on the increasing importance of ML in addressing water environment issues. ML is utilized for forecasting water quality, managing resource shortages, and optimizing allocation. It proves effective in automating water-treatment applications, monitoring natural systems, and enhancing water-based agriculture practices. ML is expected to reduce costs and provide support for complex water chemistry problems. However, challenges such as data handling, model explainability, and reproducibility hinder implementation. ML enables analysis, categorization, and optimization of water treatment systems owing to growing data volumes. It effectively handles nonlinear problems compared with traditional models. ML reduces material resource requirements for tests and research. The study highlights the ability of ML to model complex relationships and discusses its applications in assessing water quality and adsorption onto various adsorbents. It emphasizes the relevance of ML across waste-water, drinking-water, and surface-water environments. Overall, the study underscores the potential of ML to revolutionize water-management and -treatment processes.