The recent surge in online streaming platforms has complicated finding movies that match user preferences. This work shall propose an intelligent Movie Recommendation System (MRS), which will use artificial intelligence through cosine similarity to enhance the user experience. This system uses machine learning techniques to predict user behavior, past preferences, and content attributes. It computes the similarity between movies and users by the cosine similarity measure of the angle between vectors. It does so by leveraging collaborative and content-based filtering techniques that make dynamic recommendations in real time, evolving with changing user preferences. Extensive testing on diversified datasets validates the effectiveness of the system in increasing user satisfaction and the time spent with it through an elevator effect. The proposed system will, therefore, be able to show the power of AI for state-of-the-art and contextually relevant movie suggestions highly relevant to user preferences that enhance entertainment in streaming platforms. AI-driven MRS addresses the problem of content overload, presents a scalable personalized enjoyable streaming experience, and ensures 99% guaranteed content exposure.