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Depression is a highly prevalent mental illness that poses significant challenges to global public health. Although various treatment options exist, their effectiveness is often limited by patient heterogeneity and the lack of individualized treatment strategies. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a safe and noninvasive intervention for depression. However, current clinical practices typically rely on subjective or empirical decision-making and often lack personalized rTMS protocol selection, leading to suboptimal outcomes. To address this issue, we propose an artificial intelligence (AI)-powered model for personalized rTMS treatment recommendation. The model leverages multisource patient profiles, including demographic information, clinical depression data, and rTMS treatment parameters. Unlike conventional approaches, our method uses contrastive learning (CL) to enhance patient representation learning, enabling the model to better capture similarities between patient profiles. Treatment recommendations are generated by identifying the Top-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> most similar patients in the learned embedding space. Additionally, a similarity-based grouping strategy is introduced to divide the dataset into two subgroups, one receiving personalized rTMS protocols and the other receiving standard protocols. Experimental results show that BERT-based models achieve over 77.3% accuracy in Acc@Top-5. Furthermore, patients who received AI-suggested personalized rTMS protocols exhibited greater improvements in psychometric scale scores and larger effect sizes, demonstrating the effectiveness of our approach. Finally, we conduct an ablation study to examine the role of CL and systematically analyze the impact of different neural encoders and Top-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> values. These findings offer insights into model design and performance optimization.