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Book suggestions may be used by users to explore and search for books on the internet. Given a vast number of items and descriptions that correlate to the user's requirements, our recommendation system will assist the user in picking the book that best matches the description. The following criteria impact recommendation algorithms: rating, reviews, description, and author. The effectiveness of Book Recommendation Systems is greatly dependent on the classifier utilized. As a result, developing an accurate classifier is critical for improving the performance of recommendation systems. Decision Tree Classifiers stand out among many supervised learning approaches and algorithms due to their high accuracy, fast classification speed, strong learning ability, and ease of design. The framework for a Decision Tree-Based recommendation system is proposed in this study. Among the other significant supervised learning techniques and algorithms are Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor. [ABSTRACT FROM AUTHOR]