This study combined content-based and collaborative filtering algorithms to create an all-inclusive movie recommendation system. In order to find trends and provide suggestions based on the tastes of comparable users, collaborative filtering analyzes user–item interaction data. Movie qualities are evaluated using content-based filtering in order to suggest related products. Preprocessing techniques like data cleansing, filtering, and text processing are used in the implementation to extract pertinent information and textual elements. While the textual characteristics are converted into numerical representations using methods like literal_eval and CountVectorizer, the metadata contains information on genres, release dates, cast, crew, and keywords. Using the vectorized characteristics of two videos, the cosine similarity between them is computed. Techniques for collaborative and content-based filtering are used to deliver accurate and customized.