The integration of music and the study of facial expressions offers an exciting avenue for enhancing the therapeutic impact of music in the field of healthcare and emotional well-being. The abstract presents a novel approach to music therapy by combining the realms of music and facial expression analysis through a real-time expression capturing algorithm. This algorithm, based on Convolutional Neural Network (CNN) models, demonstrates an impressive accuracy of 98% in capturing user expressions even in diverse lighting conditions. The objective of this integration is to enhance the therapeutic impact of music within the healthcare domain and promote emotional well-being. In this study, the therapy process involves accessing music files based on the real-time analysis of the user’s facial expressions. The captured expressions are utilized as input to determine the emotional state of the user. The accuracy of the expression capturing algorithm ensures reliable and precise identification of emotions, contributing to the effectiveness of the therapy. The evaluation of user satisfaction is conducted through a four-stage measurement process. The results indicate that users find the therapy satisfactory, affirming the potential of this innovative approach to music therapy. This research opens up new possibilities for leveraging technology, specifically CNN algorithms, to create personalized and responsive music therapy interventions that cater to individual emotional states in real time. The findings suggest promising applications in the broader context of healthcare, highlighting the significance of integrating advanced technologies to enhance the therapeutic experience.