Accurate prognostic biomarkers are essential for evaluating survival risks in cancer patients. However, despite the wide use of biomarkers like prostate-specific antigen (PSA) and other clinical factors, achieving high predictive accuracy remains a challenge in prostate cancer prognosis. This study aimed to predict 24-month mortality in metastatic castration-resistant prostate cancer (mCRPC) patients by analyzing a comprehensive set of 41 clinical and demographic features. A cohort of 703 patients was assessed, and machine learning models, including XGBoost, SVM, and Random Forest, were compared. Of these, the Random Forest model demonstrated the highest performance, achieving an accuracy of 0.67 and an AUC of 0.68, effectively distinguishing between patients with less than 24 months of survival and more than 24 months of survival. Predictors identified in our analysis included PSA, albumin, and lactate dehydrogenase (LDH). These findings suggest that clinical factors can be effectively utilized in machine learning models to predict mortality outcomes in cancer patients.