Prediction of postoperative complications is crucial in surgical care, particularly for penile prosthesis implantation. We retrospectively evaluated demographic, clinical, laboratory, and surgical data from patients who underwent penile prosthesis implantation between 2015 and 2023. Six machine learning algorithms—Gradient Boosting (GB), AdaBoost, Support Vector Machine (SVM), Random Forest (RF), XGBoost (XGB), and Naive Bayes (NB)—were trained to predict the occurrence and severity of postoperative complications. Model performance was assessed using accuracy, F1 score, sensitivity, specificity, Youden Index, and AUC value, and statistical comparisons identified the most effective approach. The GB model achieved the highest F1 score (0.86 ± 0.09; 95% CI, 0.84–0.87), significantly outperforming other models. Sensitivity was greatest for GB and XGB (0.78 ± 0.13; 95% CI, 0.75–0.80), with GB superior to AdaBoost and SVM (p < 0.001). RF demonstrated the highest specificity (1.00 ± 0.00; 95% CI, 1.00–1.00), exceeding AdaBoost, SVM, and NB (p < 0.001). GB best predicted mild complications (0.74 ± 0.14; 95% CI, 0.72–0.77), while NB excelled for severe complications (0.94 ± 0.17; 95% CI, 0.90–0.98). Overall accuracy was 0.90 ± 0.04 (95% CI, 0.89–0.90) for both GB and RF. Feature analysis highlighted HbA1c, total testosterone, and urea as key predictors. Implementing GB-based machine learning may enhance surgical decision-making in this setting.