The purpose of this research is to analyze and compare the various AI-based recommender systems that are currently available. The purpose of the research is to offer a complete review of the many methods and approaches that are utilized in recommender systems, as well as to analyze the strengths and limitations of each of these techniques and approaches. A wide variety of subjects, such as collaborative filtering, content-based filtering, matrix factorization, deep learning, and hybrid recommender systems are discussed in the literature review. In addition to this, a case study is provided to show the use of AI-based recommender systems in a real-world setting. In last, a comparison table is offered to briefly outline the most important aspects shared by each of the various recommender systems. The findings of this research can provide researchers and practitioners working in the field of recommendation systems with a better understanding of the various methods that are currently available, as well as assistance in selecting the strategy that is best suited for the particular application they are working on.