In today’s data-rich environment, the vast amount of information available to users presents them with countless options. Therefore, the need for an appropriate recommendation system is crucial to ensure that items are suggested to users based on their personal biases and preferences. A book recommender system utilizes historical data to recommend books to users, carefully considering the preferences of each individual. Our study aims to redefine book recommendations by integrating cutting-edge information retrieval techniques with evolving reader preferences. Existing systems often extract irrelevant information, leading to user dissatisfaction. In this paper, we introduce BookNest: An Ensemble Book Recommendation System. Our proposed system revolves around three key components: a Recommendation Engine employing collaborative filtering, genre-based filtering, and popularity-based filtering, and an Ensembling algorithm that consolidates recommendations from various algorithms to produce efficient and effective recommendations. BookNest is evaluated using multiple parameters, and the results demonstrate that the proposed model outperforms traditional recommender systems.