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This research paper introduces a novel personalized music recommendation system that leverages collaborative filtering and semantic analysis techniques. The system aims to generate customized music recommendations tailored to individual users' preferences and listening histories. By employing collaborative filtering, the system identifies users with similar music tastes and suggests music they enjoy. Additionally, semantic analysis is utilized to analyze the current sentiment of a user, enabling the system to recommend songs suitable for the user's mood for initial recommendations. A dataset containing user listening histories and music metadata is utilized to evaluate the system's performance. The results demonstrate that the proposed system surpasses traditional collaborative filtering approaches in identifying the coldstart issue for new users. This capability enhances the music listening experience by providing more relevant and personalized recommendations while facilitating the discovery of new and captivating music. The system's unique approach of combining collaborative filtering and semantic analysis provides a fresh perspective on personalized music recommendations, contributing to the advancement of music recommendation systems. The evaluation outcomes signify the promising nature of the proposed method. Future research can focus on further optimizing the system's performance through additional refinements and evaluations. [ABSTRACT FROM AUTHOR]