Water, one of humanity's most precious natural resources, plays a pivotal role in both ecosystem health and human wellbeing. It is indispensable for drinking, agriculture, and industrial processes. However, over the years, various pollutants have increasingly jeopardized water quality. Consequently, predicting and estimating water quality has become essential to mitigating water pollution. Traditional methods of water quality assessment, which rely on costly laboratory and statistical analyses, are inadequate for real-time monitoring. Therefore, there is an urgent need for a more practical and cost-effective solution to ensure water quality.This study presents an innovative approach to water quality classification using machine learning techniques, specifically the Gradient Boosting Classifier. The proposed system aims to develop a model capable of forecasting the Water Quality Index (WQI) and classifying water quality into distinct categories. The method involves calculating the WQI, a critical measure of water quality, utilizing various parameters such as pH, dissolved oxygen, temperature, and electrical conductivity. The developed model achieves a remarkable accuracy rate of 98%, effectively predicting water quality as Excellent, Good, Poor, or Very Poor.The efficacy and precision of this machine learning-based approach underscore its potential for real-time water quality monitoring and management. The results highlight the significant advantages of employing machine learning techniques for environmental applications. This system can be instrumental in diverse contexts, including water treatment, environmental monitoring, and the management of aquatic life, offering a robust solution for maintaining and enhancing water quality.