The rapid growth of digital music consumption has revealed clear limitations in traditional recommendation systems that rely only on collaborative filtering or genre-based similarity. This research introduces an intelligent playlist recommendation framework that brings together multiple forms of user information, including listening behavior, contextual patterns, social interactions, and cultural dynamics. The framework is designed to overcome common challenges such as cold start conditions, popularity bias, and restricted diversity in recommendations. A distinctive strength of the system is its ability to adapt in real time to changes in user mood, evolving preferences, and emerging music trends. This ensures that the generated playlists are not only accurate but also socially relevant and contextually appropriate. The proposed approach enhances personalization, fosters novelty, and increases user engagement, leading to a more natural and satisfying listening experience. Furthermore, the research highlights its broader cultural value by promoting inclusivity and supporting diverse listening practices.