Objective To systematically evaluate the performance and methodological quality of the risk prediction models for urinary incontinence after radical prostatectomy, so as to provide a reference for selecting the appropriate risk prediction tool. Methods A systematic search was conducted in PubMed, Web of Science, Cochrane Library, CINAHL, EMBASE, CNKI, Wanfang, VIP, and Chinese biomedical literature database from inception to Jan. 23, 2024. Two researchers independently conducted literature screening and data extraction, and the prediction model risk of bias assessment tool (PROBAST) was applied to assess the risk of bias and applicability of the included studies. MedCalc software was used to perform a meta-analysis of the area under curve (AUC) of the validation groups using the random effect model, and the publication bias and sensitivity analysis were also performed. Results A total of 8 studies were included, with a combined sample size of 7 216 cases. Six models reported the AUC values, and 7 models reported calibration. The applicability of 2 studies was acceptable, while 6 were poor. The most commonly used type of prediction model was logistic regression. After excluding models with extreme AUC values, the random-effects meta-analysis result was 0.840 (95% confidence interval 0.786 to 0.895), with no heterogeneity (I²＝0%, P＝0.737). The bias risk was high in all 8 studies, mainly due to retrospective cohort data, transformation of continuous variables into binary variables, unaddressed missing data, selection of predictors based on univariate analysis, incomplete report of the model discrimination and calibration, and lack of external validation. Egger test result indicated no significant publication bias. Conclusion The development and validation process of the existing risk prediction models for urinary incontinence after radical prostatectomy is still imperfect. Future research should construct prediction models based on multicenter and large-sample data, strengthen the clinical applicability assessment of the models, and strictly follow the reporting standards and procedures, so as to establish high-quality risk prediction models for clinical practice.