Precise water quality forecasting is critical for the effective management of aquatic resources and the mitigation of environmental pollution. With increasing pressures from climate change, industrialization, and urban expansion, forecasting crucial parameters such as turbidity, pH, Nitrogen, DO (Dissolved oxygen), and contaminant concentrations has become essential. This review examines 25 recent studies employing advanced deep learning approaches like CNN–LSTM, CNN–GRU, Bi-LSTM with attention, and hybrid decomposition-based architectures—for water quality prediction. This research affords a comprehensive investigation of traditional water quality prediction methods and offers a technical overview of their application in predicting water quality. It presents an detailed review of the current state of water quality prediction, highlighting the key characteristics of emerging technologies in this field. Real-time datasets are commonly utilized, and RMSE, MSE, and MAPE are the most widely adopted performance metrics. Despite promising results, the models reviewed exhibit limitations in scalability, interpretability, and adaptability to spatial-temporal variability. The review underscores the need for future research to explore lightweight, transfer-learning-enabled models with adaptive decomposition capabilities to enhance prediction accuracy, efficiency, and real-time application across diverse environments.