<b><i>Introduction:</i></b> The study aimed to construct and validate a risk prediction model for incidence of postoperative renal failure (PORF) following radical nephrectomy and nephroureterectomy. <b><i>Methods:</i></b> The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database years 2005–2014 were used for the derivation cohort. A stepwise multivariate logistic regression analysis was conducted, and the final model was validated with an independent cohort from the ACS-NSQIP database years 2015–2017. <b><i>Results:</i></b> In cohort of 14,519 patients, 296 (2.0%) developed PORF. The final 9-factor model included age, gender, diabetes, hypertension, BMI, preoperative creatinine, hematocrit, platelet count, and surgical approach. Model receiver-operator curve analysis provided a C-statistic of 0.79 (0.77, 0.82; <i>p</i> < 0.001), and overall calibration testing <i>R</i><sup>2</sup> was 0.99. Model performance in the validation cohort provided a C-statistic of 0.79 (0.76, 0.81; <i>p</i> < 0.001). <b><i>Conclusion:</i></b> PORF is a known risk factor for chronic kidney disease and cardiovascular morbidity, and is a common occurrence after unilateral kidney removal. The authors propose a robust and validated risk prediction model to aid in identification of high-risk patients and optimization of perioperative care.