Background: Genetic testing for pathogenic DNA damage repair gene (pDDRg) mutations has clinical benefits for prostate cancer (PCa) patients, but its real-world application faces challenges due to its high associated costs. We sought to develop a magnetic resonance imaging (MRI)-based radiomics model capable of assessing the likelihood of PCa patients harboring pDDRg mutations. We then rigorously validated its predictive value in two external validation cohorts. Methods: A total of 225 patients with both multiparametric MRI data before prostate biopsy and genetic testing information for pDDRg mutations were included in this study. The radiomics features were extracted from the T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences of the MRI images in the training cohort (N=101) using the least absolute shrinkage and selection operator (LASSO) algorithm. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) were used to validate the predictive value of the model in both the internal (N=41) and external (N=83) validation cohorts. Results: In total, 48 of the 225 (21.3%) patients in our cohort were identified by genetic testing as having positive pDDRg mutations, including BRCA1/2 (N=13), CDK12 (N=15), ATM (N=9), and other pDDRg mutations (N=17). Thirteen radiomics features from T2WI (N=7) and ADC sequences (N=6) were extracted to develop a model predicting pDDRg mutation carriers. The radiomics-based model had AUC values of 0.824 [95% confidence interval (CI): 0.677-0.923] in the internal validation dataset and 0.836 (95% CI: 0.738-0.908) in the external validation dataset. Notably, setting the cut-off value as "zero misseddignoses" resulted in a potential reduction of around 25% in unnecessary gene testing across both the internal and external validation datasets. Conclusions: Our MRI radiomics-based predictive model is a promising pre-testing tool for pDDRg mutation prediction in patients with PCa. Prospective studies need to be conducted to further validate the power of this predictive model before its clinical application.