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Listening to music has become one of the most frequently resorted to pastimes of people ranging from the youth to the elder. While there are umpteen songs of different genres and artists from yesteryears in the podcast, it becomes essential that there is a recommendation System that analyzes the liking of a specific user with the help of the datasets genre and artists of the songs that he/she listened to in the past three days. The goal of this project is to create a system for recommending music that will analyze user interactions with the app or music platform in order to establish their musical preferences. Our system learns from users’ previous listening history and recommends music they want to listen to in the future. Currently music service providers have generic, mood-based playlists, that are the same for all users. Here, we suggest improvements to these playlists by offering custom playlists for each user based on user input. Rich web application technologies have proliferated as a result of the rise in Internet usage as a source of information. Users can use these devices to listen to music without having to download it to their PC. Some people additionally employ their preferred methods to enhance the user experience.