Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments.