Objective To compare the efficacy of various machine learning models in predicting renal function decline after robot-assisted partial nephrectomy（RAPN），and to provide evidence for clinical risk stratification. Methods This study retrospectively included the clinical data of 733 patients with renal cell carcinoma undergoing RAPN at the Urology Department of The First Affiliated Hospital of Chongqing Medical University from January 2019 to December 2023. Demographic characteristics，laboratory indicators，and perioperative parameters were integrated to construct seven machine learning models. Key predictors were interpreted using Shapley additive explanations（SHAP）. Model performance was evaluated using the area under the receiver operating characteristic curve （AUC）. Results The random forest model demonstrated the best predictive performance（AUC=0.84）. SHAP analysis identified neutrophil-to-lymphocyte ratio，tumor diameter，the international normalized ratio of prothrombin time，white blood cell count，and intraoperative blood loss as significant factors influencing postoperative renal function decline. Conclusion This study provides a potential predictive tool for clinical practice，aiding in identifying high-risk patients and optimizing postoperative management strategies.