Environmental sustainability and human health depend on water quality prediction. Water quality characteristics are predicted using modern Machine Learning (ML) techniques in this study. The goal is to improve water quality assessments with robust predictive models. The research collects large datasets of chemical, biological, and physical water quality factors. The work focuses on developing and evaluating ML models to anticipate water quality metrics across time. Models learn patterns, trends, and anomalies from past data. Model dependability and generalization are assessed using cross-validation and performance indicators. This research seeks to illuminate Machine Learning's prediction powers in water quality management. Environmental agencies, researchers, and policymakers can use the proposed models to make water resource management and pollution control decisions.