Objective To develop a nomogram model for predicting pelvic lymph node metastasis in prostate cancer patients based on clinical, magnetic resonance imaging (MRI), and biopsy pathological features, and to evaluate its diagnostic performance. Methods Patients who were diagnosed with prostate cancer and underwent radical prostatectomy plus extended pelvic lymph node dissection at our hospital from Jan. 2013 to Jun. 2024 were retrospectively enrolled. The clinical, MRI, and biopsy data were collected. The least absolute shrinkage and selection operator (LASSO) regression and binary multivariate logistic regression were used to identify independent predictors for pelvic lymph node metastasis, which were then incorporated to construct a nomogram model. The performance of the nomogram model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results A total of 538 patients with prostate cancer were included. Age, maximum diameter of tumor, clinical T stage, prostate-specific antigen density, biopsy International Society of Urological Pathology grade, and European Society of Urogenital Radiology score were identified by LASSO regression and binary multivariate logistic regression as independent predictors for pelvic lymph node metastasis in patients with prostate cancer; these variables were incorporated into the nomogram model. The area under the ROC curve of the nomogram for predicting pelvic lymph node metastasis in prostate cancer patients was 0.88 (95% confidence interval ［95%CI］ 0.84-0.92). The mean C-index of the model was 0.877 (95%CI 0.862-0.884), indicating good stability. The model provided greater net benefit than either the “treat-all” or “treat-none” strategies across threshold probability ranges of 0.01-0.75 and 0.81-0.99. Conclusion The nomogram model constructed using routine clinical, MRI, and biopsy pathological features can predict the risk of pelvic lymph node metastasis in patients with prostate cancer. This model may provide a scientific basis for determining lymph node dissection strategies and postoperative adjuvant treatment decisions.