Water is a valuable natural resource and national asset, and the primary component of ecosystems. Water sources include rivers, lakes, glaciers, rainwater, and groundwater. Water resources are essential for many economic sectors, including agriculture, animal production, forestry, industrial operations, hydropower generation, fisheries, and more. Water availability and quality are deteriorating due to factors such as population growth, industry, and urbanization. The Water Quality Index (WQI) is a useful and exclusive classification system that summarizes all aspects of water quality in a single phrase. This rating system aids in selecting the most suitable treatment method to address the challenges. The models were evaluated using key statistical factors, a dataset with six relevant parameters, and water use records. The database included electrical conductivity, pH, dissolved oxygen, nitrates, phosphates, suspended particles, and water temperature. We used three machine learning models for the classification of water namely, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF). This study was conducted on Keenjhar Lake, Karachi, Pakistan, and this work employed a dataset of 360 instances and six defining features from 1993 to 2022 on the monthly dataset. The classification algorithms were evaluated using five metrics: accuracy, recall, precision, Pearson’s correlation, and F1 score. In terms of classification, the testing results indicate that the SVM model performed the best, predicting Water Quality Classification (WQC) values with an accuracy of 99.50%. It is important to note that precise water quantity and quality predictions are vital for sustainable resource management, public health protection, and environmental preservation.