Eutrophication is one of the most challenging environmental problems facing surface water at present, and existing studies mostly focus on regional-scale analysis. To reveal the eutrophication patterns and their complex driving mechanisms in large-scale river basins, this paper evaluated and classified the eutrophication status of river basins across China and explored the potential driving factors affecting the changes in eutrophication status by combining machine learning and the explainable artificial intelligence method of SHapley Additive exPlanations (SHAP). The results show that eutrophication is widespread in most river basins, and the eutrophication problem in eastern, northeastern, and central China is more severe than that in western regions, exhibiting significant spatial-temporal clustering characteristics; total phosphorus and total nitrogen are key water quality factors, with contributions of 20.6% and 75.4%, respectively, and both have a significant positive spatial correlation with river basin eutrophication; the Light Gradient Boosting Machine (LightGBM) model performs best among all models, with an accuracy of 91%. SHAP analysis reveals that compared with anthropogenic factors, natural driving factors play a more important role in explaining the overall changes in the eutrophication status of river basins, among which topographic conditions control the accumulation and loss of nutrients, while precipitation plays a regulatory role. However, with the aggravation of eutrophication, the impact of anthropogenic factors such as population density, gross domestic product, and wastewater treatment capacity gradually becomes prominent. If economic development is accompanied by the improvement of infrastructure, eutrophication can be mitigated to a certain extent. Given the synergistic effect of internal nutrient loads and external river basin characteristics on eutrophication, it is recommended to implement precise management and control based on spatial zoning and classification to effectively address this environmental challenge. This study deepens the theoretical understanding of the driving mechanism of river basin-scale eutrophication by combining artificial intelligence methods and provides quantitative and interpretable scientific evidence for water environment governance.