<p>TME represents the established surgical approach for mid-low rectal cancer and improves survival, yet carries a substantial risk of postoperative urinary dysfunction that severely impacts quality of life. Data were retrospectively collected from two centers. A modeling cohort of 290 patients from center one was split 7:3 into a training set (n = 203) and an internal validation set (n = 87). Stepwise logistic regression analysis identified independent risk factors and incorporated them into the model, which was subsequently validated both internally and externally using 122 patients from Cohort 2. Model accuracy was evaluated using receiver operating characteristic (ROC) curves, consistency was assessed with calibration curves, and clinical benefit was evaluated by decision curve analysis (DCA). Multivariate regression identified six independent influencing factors: age (OR = 1.50, P = 0.047), neoadjuvant radiotherapy (OR = 2.40, P = 0.025), low tumor location (OR = 2.42, P = 0.022), low ligation of the inferior mesenteric artery (IMA) (OR = 0.38, P = 0.013), tumor diameter &gt; 5 cm (OR = 3.85, P = 0.001), and number of lymph nodes dissected (OR = 1.23, P = 0.003). The resulting predictive model exhibited favorable discrimination across datasets, with area under curves (AUC) of 0.83 (training), 0.78 (internal validation), and 0.75 (external validation). Good model fit was observed in calibration analysis, and decision curve analysis indicated a meaningful clinical net benefit. This study developed and validated a nomogram to predict the risk of postoperative urinary dysfunction after robot-assisted total mesorectal excision. The primary aim of this tool is to enable early identification of high-risk patients, allowing for timely preventive and therapeutic interventions, thereby optimizing functional recovery and improving postoperative quality of life. The model demonstrates good discrimination and offers meaningful clinical utility in supporting individualized patient management.</p>