Transcranial Magnetic Stimulation (TMS) efficacy relies on accurate determination of the resting motor threshold (RMT), which is currently determined manually. While previous studies have identified various anatomical and physiological factors influencing RMT, their analyses remained primarily descriptive. This study introduces a machine learning approach to predict RMT levels using anatomical MRI, EEG, and simulated E-field features. We analyzed data from 17 subjects with mild-to-moderate traumatic brain injury, implementing a three-phase analytical approach: independent analysis of feature groups, evaluation of feature importance, and development of a final predictive model. Our results confirmed previously identified RMT descriptors (gray matter percentage, brain-scalp distance, maximum E-field intensity) and revealed novel anatomical factors, including volume percentages of skull, white matter, brain, and skin. EEG analysis showed that beta band activity and delta band connectivity at specific channels were predictive of RMT levels. The optimal model, an SVM with radial basis function kernel, achieved the highest stability in crossvalidation and 100% balanced accuracy score on the test set. This work represents the first machine learning model for RMT prediction, contributing to the development of more efficient, personalized TMS protocols.