<p>Urbanization and industrial growth have severely impacted water quality, creating urgent demand for intelligent prediction frameworks to support sustainable urban water management. Traditional monitoring methods are often limited by their inability to capture real-time or high-resolution data, necessitating data-driven alternatives. This study proposes a hybrid deep learning and machine learning framework that classifies Water Quality Index (WQI) categories using Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Multi-Layer Perceptron (MLP). CNN processes time-series groundwater data as 2D matrices, leveraging dilated convolutions and regularization techniques to extract multiscale temporal patterns. The model was trained and validated on post-monsoon groundwater datasets from Telangana, India, employing fourfold cross-validation. Among the algorithms tested, CNN achieved the best performance with RMSE: 0.0654 and R²: 0.9981, reducing prediction error by 18–48% over KNN, NB, and MLP. These results highlight CNN’s superior ability to learn from spatial–temporal dynamics while maintaining computational efficiency. Unlike previous studies focused solely on regression models or singular algorithms, this study combines multiple classifiers into a unified prediction pipeline, enhancing adaptability across heterogeneous datasets. Furthermore, the model’s strong performance relative to state-of-the-art hybrid architectures such as LSTM-GRU and attention-based networks demonstrates its suitability for deployment in regions lacking dense sensor infrastructure. The findings support real-time pollution classification, guiding policy decisions for targeted remediation. Future implementations could integrate real-time sensor data and employ federated learning for broader applicability. This framework offers a robust, scalable, and interpretable solution for sustainable water quality management amid rapid urban expansion and climate variability.</p>