Purpose: Determining the optimal surgical approach for patients with T1 renal tumors requires balancing long-term oncological and renal functional outcomes. Using machine learning algorithms, we aimed to develop a model to predict both outcomes simultaneously, according to each radical (RN) and partial nephrectomy (PN). Materials and Methods: Using demographic and preoperative variables of 823 patients with clinical T1N0M0 renal tumors who underwent PN or RN between 2007 and 2019, we employed 5 different machine learning algorithms—general linear model (GLM), extreme gradient boosting (XgBoost), gradient boosting machine, distributed random forest, deep learning—and compared to predict recurrence probability and estimated glomerular filtration rate (eGFR) at 5-year after surgery. Model performance for recurrence prediction was evaluated with area under the curve receiver operating characteristic, area under the precision-recall curve, and log-loss, while eGFR prediction was assessed using root mean square error (RMSE) and R2. Results: Of the 823 patients, 463 (56.3%) had T1a tumors and 487 (59.2%) underwent PN. The median preoperative eGFR was 99.1 mL/min/1.73 m2, and at 5 years postoperative it was 70.4 after RN and 92.0 after PN. Recurrence within 5 years was observed in 1.1% and 4.2% of T1a and T1b cohorts, respectively. We developed models based on clinically significant preoperative variables. Across the models, the XGBoost demonstrated the highest accuracy for predicting 5-year recurrence, with superior recall (0.0252) and precision (0.0465) compared to other algorithms. For 5-year eGFR prediction, the GLM outperformed other models, achieving RMSE of 12.700 and R2/sup> of 0.694 on the test set. The 2 models were integrated into a single online interface. Conclusion We developed a tool to reliably predict 5-year oncological and renal functional outcomes following each nephrectomy type in patients with T1 renal tumors. Further multi-institutional validation is needed to confirm its generalizability and applicability across diverse clinical settings.