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One of the most common uses of machine learning is recommendation systems. A broad description of recommendation systems is that they aid in predicting user ratings or preferences for an item. Their major purpose is to provide suggestions based on previously collected data or user preferences. Recommendation systems study how a user interacts with a system or application and forecast the user's future behavior. They analyze information using information filtering approaches to offer consumers possibly more relevant content. They have grown in popularity in recent years due to their goal of providing individualized experiences to each user and assisting them in making better decisions. In this study, we looked at some of the most often used movie recommendation systems, such as content-based techniques, goods, or products recommended to consumers by comparing and contrasting them based on a variety of characteristics. The collaborative filtering approach looks for commonalities between users and suggests things or goods that are likely to pique their interest. Hybrid filtering is a hybrid of the two methods. We also go through the different similarity metrics and approach performance indicators briefly. This study is concerned with comprehending various strategies for movie suggestions and how recommender systems shape today's economy. [ABSTRACT FROM AUTHOR]