(untitled) (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2025)
Smartphone music player technology is advancing rapidly. Users can now access millions of tracks online. One of the toughest challenges is choosing favourite songs from these massive libraries. Each user has unique music tastes. Music choice depends on mood and environment. Every day, new people and goods appear, and the system must respond quickly. Music recommendation algorithms have transformed music discovery and listening by customising playlists. This study personalises and emotionalises recommendations using real-time face expression analysis. The technology enhances your music listening experience by merging computer vision and emotion recognition. This project affects more than music platforms and content distribution. Discover and express your emotions through music in the global language with rapid emotional insight. This research will also change the conversation about data protection, responsible data use, and emotion-based computing. It connects our emotions to the digital world in a new way. A music recommendation system gives consumers personalised song suggestions based on their listening history and interests. To find patterns and provide recommendations, machine learning algorithms analyse users’ listening habits, song and artist data. This paper describes the machine learning music recommendation system development process, including data collection, preprocessing, feature extraction, model selection, training, recommendation production, and evaluation. The technology gives consumers personalised music recommendations based on their tastes to improve music discovery. A music recommendation system can help consumers find new songs and make music listening more fun.