The quality of water is a crucial criterion for various aspects such as public health, environmental sustainability, and economic growth. The health of individuals largely depends on the quality of the water they consume, making regular monitoring essential. Such monitoring can help in detecting outbreaks of waterborne diseases like cholera and typhoid, which pose significant public health risks. Additionally, the aquatic ecosystem relies on a delicate balance of chemical and physical properties; effective quality checks can identify pollution sources that may disrupt this balance and harm aquatic life. The agricultural and food industries also heavily depend on good quality water for crop irrigation and food processing, making water quality a vital factor in food security and safety. The proposed model “Aqua Purity - Water Quality Analysis using Machine Learning,” as the name suggests, aims to analyses water quality by training a model using various machine learning algorithms. This analysis will assist in identifying pollutants, detecting anomalies, and classifying water bodies based on quality indicators such as pH, chemical composition, and microbial presence. By integrating real-time data collection and advanced analytical techniques, the project seeks to develop a predictive framework that can forecast potential water quality issues, allowing for timely intervention. Ultimately, this initiative strives to promote better water management techniques, better public health, and contribute to sustainable environment, ensuring that water resources are preserved for future generations.