<p>The transfer of moments between critical and non-critical regions is a key behavior in statically indeterminate reinforced concrete (RC) structures, enhancing the utilization of less critical sections and improving the overall ductility and safety of the structural system. Accurate prediction of moment redistribution remains challenging with traditional experimental and analytical methods. This study addresses this critical gap by developing a machine learning (ML) model to estimate the degree of moment redistribution in RC flexural members based on key structural and material parameters. A dataset of 103 experimental results compiled from the literature was used to train and validate the ML models. Among the algorithms investigated, the fine-tuned Random Forest regression model achieved the highest accuracy, with a coefficient of determination (R²) of 0.90. A Variable Importance Measure (VIM) was employed to assess the relative influence of each input parameter on moment redistribution. VIM highlighted the significant roles of tensile reinforcement ratios and the ratio of neutral axis depth to effective section depth on moment redistribution. Additionally, the VIM findings were benchmarked against existing experimental and numerical parametric studies, showing strong agreement. These results provide a practical, efficient tool for predicting moment redistribution, contributing to safer and more economical RC structural designs while improving computational efficiency. </p>