Music therapy offers a non-invasive alternative; however, its effectiveness depends heavily on individualized emotional matching. To address this limitation, this study proposes an Intelligent Music Therapy System that integrates Music Affective Computing Models with Internet of Things–based wearable sensing for personalized emotional intervention. The system incorporates a deep learning–based Music Affective Computing Model to recommend music and an IoT framework to continuously acquire physiological signals, including heart rate variability, from elderly users. Six representative Music Affective Computing Model architectures were systematically evaluated, among which a hybrid Fractal Convolution Neural Network–Long Short-Term Memory–Transformer model demonstrated the highest classification accuracy and generalization stability in Chinese classical and ethnic music emotion recognition. To validate clinical applicability, an intelligent music therapy system was deployed in a randomized controlled group music therapy trial for elderly individuals. Experimental results indicated significant improvements in heart rate variability indices and depressive mood scores compared with the control condition; therefore, the proposed system can effectively support emotion-aware music intervention in nursing home environments.