<p>The survival of ecosystems, agriculture, industry, and drinking water supply depends on rivers, which are among the most vital freshwater resources. The focus of this study is on long-term river modeling. This study examines how machine learning (ML) models can predict river discharge and incorporates explainable artificial intelligence (XAI) principles to enhance model transparency and reliability. Bootstrap Tree (BT), Decision Tree (DT), Histogram Gradient Boosting (HGB), Extreme Gradient Boosting (XGB), and Categorical Boosting (CB) are among the models that were examined. A comprehensive set of performance metrics was used to assess these models and they were verified through rigorous validation. The study area was the South Platte River in the United States, which is a hydrological system that is increasingly affected by discharge variability due to climate change. The results demonstrate CB, XGB, and HGB’s superior predictive accuracy and minimal errors. The top-performing models demonstrated high performance indices with coefficients of determination (R²) of 0.939, 0.852, and 0.817, respectively. Long-term modeling indicates a continuous decline in streamflow, with projections suggesting potential drying within the next two decades if no intervention occurs. These findings underscore the need for urgent action to address water resource challenges and are closely aligned with the United Nations Sustainable Development Goals (SDGs), especially SDG 6, SDG 13, and SDG 15.</p>