In the digital age, the overwhelming availability of books across various genres and formats makes it challenging for readers to discover literature that aligns with their preferences. This study presents a machine learning-powered book recommendation system that provides personalized suggestions to enhance user experience. The system integrates collaborative filtering and content-based filtering techniques to analyze user preferences and book characteristics, ensuring relevant recommendations. The dataset comprises user ratings, book descriptions, and metadata, which are processed to extract meaningful features. Machine learning algorithms, including k-Nearest Neighbors (k-NN) and matrix factorization techniques, are employed to train the model and identify patterns in user behavior. To ensure recommendation accuracy, the system's performance is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results indicate the effectiveness of the proposed approach in delivering tailored book suggestions, enhancing the reading experience. Furthermore, this work highlights future improvements, including the integration of natural language processing (NLP) for enhanced content evaluation and user engagement, showcasing the potential of AI-driven recommendation systems in the literary domain.