Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are well-established predictors, the added value of preoperative MRI remains underexplored. We assess whether MRI provides additional predictive value for ED at 12 months post-surgery, evaluating four strategies: (1) a clinical-only baseline; (2) classical models using handcrafted MRI-based features; (3) deep learning models trained on MRI slices; and (4) fusion of imaging and clinical inputs. Imaging-based models (AUC 0.569) slightly outperformed handcrafted approaches (AUC 0.554) but fell short of the clinical baseline (AUC 0.663). Fusion models offered marginal gains (AUC 0.586) but did not surpass clinical-only performance. SHAP analysis on the fusion method confirmed that clinical features were the primary drivers of predictive accuracy, while saliency maps suggested that imaging models focused on anatomically relevant regions. However, MRI offered limited added value beyond clinical data in prediction, likely due to dataset size, imaging-related variability, and missing clinical confounders. Larger, standardized datasets and improved integration strategies are needed to better evaluate MRI’s potential in ED prediction.