Rivers and freshwater bodies are vital for ecosystems and biodiversity, and provide resources for human consumption and industry. Monitoring and forecasting water quality is essential for ecological integrity and public health. Machine learning (ML) techniques offer significant promise in this area, analyzing large datasets to predict key water quality parameters like NH4 (ammonium), which indicates both natural processes and pollution events. This research investigates the application of four ML models—Gradient Boosting Regressor, Support Vector Regressor (SVR), Long Short-Term Memory (LSTM) networks, and Multi-Layer Perceptron (MLP) Regression—for river water quality forecasting. Our study aims to compare and evaluate their performance. The findings reveal that the MLP Regression model outperformed the others, achieving a Mean Squared Error (MSE) of 0.13065. This research provides valuable insights into the efficacy of ML models for water quality forecasting, supporting decision-makers, environmentalists, and policy developers in preserving and managing freshwater resources efficiently. The comparative analysis also offers a template for similar studies in other environmental domains.