Book discovery in the digital age is extremely difficult which is the result of various factors such as users struggling with information overload as well as trying to find the content most closely to their needs. Albeit there are many recommendation systems available in the market, most of them are just based on general ratings and neither do they use the rich metadata that is available from external book sources nor do they provide the functionality of exploring related work in the best way. Through the use of our proposed book recommendation system, the constraints that are in place currently can be very easily overcome effectively by the use of more advanced reinforcement machine learning and data integration techniques. What the model would do is to analyze users’ reading history and preferences and combining data from bookstores so that an efficient and effective model would be built which would give accurate suggestions of books to the users according to their preference. The Python language is chosen as the basis for development and for the backend, the Flask framework is used while for finding the most appropriate document for the reader, TF-IDF vectorization, and cosine similarity are employed. Moreover, the linkage of outside APIs not only makes it more in-depth to look at but also increases the system’s accuracy. Our approach enables the users to discover new and personalized books in a simple and efficient manner. Our project is a direct contribution to a highly interactive reading journey and it also contributes to increasing love for literature by giving the users the opportunity to find books that will truly engage them.