Objective To analyze the risk factors in patients with prostate-specific antigen(PSA)gray zone prostate cancer and to establish a prediction model, thereby reducing unnecessary prostate biopsies in such patients. Methods A retrospective analysis was performed on the clinical data of 284 patients treated in our hospital during Apr. 2020 and Jun. 2024, who had a PSA level of 4~10 ng/mL and underwent prostate biopsy. The risk factors were identified with univariate and multivariate logistic regression analyses. A nomogram prediction model was constructed based on the significant risk factors with P＜0.05 in the multivariate logistic regression model. The calibration and clinical utility of the nomogram were evaluated using the calibration curve and decision curve analysis(DCA). Results A total of 107 cases(37.68%)were diagnosed with prostate cancer through biopsy pathology. The results of multivariate logistic regression analysis showed that older age(OR= 1.06, 95%CI:1.01-1.11, P=0.016), higher PSA density(PSAD)(OR=835.52, 95%CI:104.94-6681.65, P=0.007), and higher Prostate Imaging Reporting and Data System(PI-RADS)v2.1 score(PI-RADS score of 4:OR=14.07, 95%CI:4.37-45.37, P＜0.001; PI-RADS score of 5:OR=37.47, 95%CI:8.75-160.50, P＜0.001)were independent risk factors for the development of PSA gray zone prostate cancer. A nomogram prediction model based on the factors was constructed, and its performance was evaluated with the receiver operating characteristic(ROC)curve. The results showed that the area under the ROC curve(AUC)of the training set was 0.904(95%CI:0.863~0.944), and the AUC of the validation set was 0.815(95% CI:0.735-0.894). Calibration curve showed good consistency between the predicted probability and actual probability. DCA further validated the clinical value of the model. Conclusion The nomogram prediction model based on patients' age, PSAD, and PI-RADS v2.1 score can improve the diagnostic efficacy of prostate cancer and reduce unnecessary prostate biopsies.