Music suggestions based on elements including user listening history, music genre, etc. have become more popular in recent years. The user's emotional state is not considered by conventional music recommendation algorithms; therefore, they might not offer suggestions that are appropriate for their present mood. This study introduces a song recommendation system that uses artificial intelligence and machine learning to make individualized music suggestions based on the emotional state of the user. The system extracts feature with the help of MFCC to analyze the tone of the user. The suggested method incorporates deep learning models such as Artificial Neural Networks that provide better accuracy to train the model. The major challenge in creating such a system is to successfully determine the data for the recommendation process by accurately and consistently detecting the user's emotional state from speech. The proposed system offers new pathways for research in the fields of artificial intelligence and music recommendation and has the potential to alter the way people listen to music.
