Music recommendation systems play a crucial role in addressing the challenges of information overload and personalization in the digital music landscape. This paper presents the implementation and contribution of a novel music recommendation system that aims to enhance the user experience and overcome the limitations of existing approaches. The AI-Driven VibeBox: Adaptive Music Streaming personalized based on Emotion provides an overview of the project's architecture, methodology, and key findings, highlighting its contributions to music recommendation systems. The exponential growth of digital music platforms has led to an overwhelming abundance of music content, making it increasingly difficult for users to discover and explore new music that aligns with their preferences. Music recommendation systems have emerged as a vital tool to address this challenge, leveraging various techniques to provide personalized suggestions and enhance the user experience. MoodSync Vibebox is a music recommendation system that seeks to advance the state-of-the-art in this domain. This review paper aims to critically analyze the project's methodology, findings, and contributions, while also situating it within the broader context of music recommendation system research.