As technology advances rapidly, the volume of associated data continues to grow exponentially.. This necessitates effective screening and personalized filtering of information to cater to individual user needs. Recommender systems, designed to speculate the interests and choices of the user and on the basis of historical data, have become integral in managing the vast amounts of information available. Deep Learning, a key component in their development, employs multi-layered processing architectures to extract enhanced feature representations. This paper provides a comprehensive review of recommender systems, breaking them down as content-based, collaborative filtering, and hybrid systems. It emphasizes the significance of various deep learning techniques, in improving prediction accuracy. The paper explores the unique capabilities of each deep learning model and underscores the importance of big data and computational power in their effectiveness. As recommender systems continue to evolve, propelled by advancements in deep learning, the future promises more sophisticated and personalized recommendation experiences across various domains.