Recommender systems are more popular and increase the production costs for many service providers. Recommender systems minimize the transaction costs and improves the quality and decision-making process to users. A recommendation system is a subclass of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. In simple words, it is an algorithm that suggests relevant items to users. Today the world is an overcrowded, recommendations are required for recommending products or services. What is more, companies are able to gain and retain customers by sending out emails with links to new offers that meet the recipients' interests, or suggestions of films and TV shows that suit their profiles. In this paper, we propose a movie recommendation system that has the ability to recommend movies to a new user as well as others. Recommender systems are widely used in the Internet Industry. Services like Amazon, Netflix, and YouTube are typical examples of recommender system. This system can suggest a set of movies to the users based on the similarity (movies similarity and users’similarity). The recommendation system follows the concepts of Content Based Filtering and Collaborative filtering. This paper also shows how an API can be made for recommendation system and used by various service providers. The machine learning aspect of the project was deployed using fast API.