Water quality monitoring is essential for public health and environmental stewardship. Conventional methods, while effective, are often costly, time-intensive, and require specialized skills. In response to these limitations, this paper explores machine learning as a rapid, scalable solution to classify water quality using key parameters, including pH, turbidity, organic carbon, and contaminants. By implementing algorithms such as random forest, SVM, and other advanced models, we seek to enhance the precision of water purity assessments. This paper shows the potential of ML applications in real-time monitoring, addressing the need for accessible, cost-efficient, and accurate water quality solutions suitable for broad deployment across diverse environments.