In the rapidly evolving landscape of music recommender systems, this literature paper embarks on a comprehensive exploration of recommender systems tailored for music, specifically emphasizing content-based recommendations with an emotional orientation. The paradigm of music suggestion has witnessed remarkable growth, placing a heightened emphasis on delivering music that resonates with the emotional state of listeners. This review encapsulates the progress and utilization of content-based music recommendation systems centered on emotions, drawing insights from a wide spectrum of academic contributions. The study meticulously scrutinizes a diverse array of approaches and algorithms employed in analogous systems, encompassing techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Decision Trees, and Random Forests. In addition to providing a synthesis of the current research landscape, this study also identifies nascent patterns and envisages potential trajectories for future investigations in this dynamic domain.