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Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that affects millions of people. Repetitive transcranial magnetic stimulation (rTMS) has been recommended as a safe, reliable, non-invasive, neurostimulation therapy option for treatment-resistant depression (TRD). The effectiveness of rTMS treatment varies among individuals; thus, predicting the responsiveness to rTMS treatment can reduce unnecessary expenses and improve treatment capacity. In this study, we combined machine learning models with depression rating scales, clinical variables, and demographic data to predict the outcomes and effectiveness of rTMS treatment for TRD patients. Using the clinical data of 356 TRD patients who each received 20 to 30 sessions of rTMS treatment over a 4-6-week period, we examined the predictive value of different depression rating scales and models for various prediction outcomes, at multiple time points. Our optimal baseline models achieved area under the curve (AUC) values of 0.634 and 0.735 for treatment response and remission prediction, respectively, using the Elastic Net. In the longitudinal analysis, using baseline data and early treatment outcomes for 1–3 weeks, all predictive values improved compared with baseline models. In addition, predicting the percentage of symptom improvement was also feasible using longitudinal treatment outcomes, achieving coefficients of determination of 0.277, at the end of week 1, and 0.464, at the end of week 3. We found that the use of depression rating subscales, combined with clinical and demographic data, including anxiety severity, employment status, age, gender, and education level, may produce higher accuracy at baseline. In the longitudinal analysis, the total scores of depression rating scales were the most significant predictors, allowing prediction models to be built using only the total scores, which resulted in high predictive value and interpretability. This work presented a convenient and economical approach for the prediction of rTMS treatment outcomes in TRD patients, using pre-treatment clinical and demographic data alone, without requiring expensive biomarker data. The predictive value was further enhanced by adding longitudinal treatment outcomes. This method is a plausible approach that could be utilized in clinical practice for individualized treatment selection, leading to better treatment outcomes for rTMS in TRD patients.