Artificial Intelligence (AI) has changed how people find, listen to, and interact with music. AI-powered recommendation systems are a key part of the personalized experiences that streaming services such as Spotify, Apple Music, and YouTube provide to their users. These recommendation systems match listeners’ tastes and interests but also work to influence consumers’ preferred behaviour and ultimately reshape the global music industry. Here we have a detailed exploration of how AI operates within music recommendation systems, helps shape consumer behaviour, and the wider implications beyond music recommendation systems themselves. To achieve this, we draw upon some technological fundamentals, real-world examples, and academic literature to analyse the opportunities and challenges posed by AI-driven, personalized experiences. The paper talks about the analytics over the data to perform certain action and then take knowledgeable actions on it. The paper is analysed using machine learning algorithms on different parameters like precision, recall and accuracy scores. Confusion matrix is drawn to understand the implication of data on the consumer preferences.