The project aims to develop a book recommendation system tailored to support and inspire individuals with an interest in reading. Leveraging a collaborative filtering approach that incorporates collaborative filtering based on K-nearest neighbors (KNN) the system identifies similarities among users or items based on their book interactions and book ratings. Through meticulous dataset preprocessing, including feature extraction of genre, author, and user preferences, the system ensures high-quality recommendations. Evaluation metrics such as precision and recall gauge system performance, while a user-friendly interface provides easy access to personalized book suggestions. Continuous user feedback drives ongoing improvements, fostering a culture of reading discovery and habit cultivation. Ultimately, the deployment of this system aims to encourage individuals to explore new literary works and develop a lifelong passion for reading.