Water quality prediction plays a significant role in safeguarding human health, preserving aquatic ecosystems, supporting sustainable water management practices, and ensuring regulatory compliance in aquatic environments. This study explores the use of machine learning (ML) models to predict water quality in various aquatic environments. By analyzing a comprehensive dataset of water quality indicators like pH, dissolved oxygen, and turbidity, the research employs several ML algorithms including Random Forest, Support Vector Machines, and Gradient Boosting Machines. Through rigorous training, validation, and optimization, the models are evaluated for their accuracy, sensitivity, and error rate. Additionally, the study identifies key factors impacting water quality variations through feature importance analysis. The study provides valuable insights for environmental monitoring, resource management, and regulatory compliance. Integrating advanced ML techniques with water quality assessment, this research aims to contribute to the development of effective early warning systems and decision-support tools that promote sustainable water management practices.