Movie recommendation systems help movie enthusiasts by suggesting movies to watch without the hassle of having to go through the time-consuming process of deciding from a large collection of movie streaming platforms that recommend movies and TV episodes. News organizations that suggest articles to readers, and online stores that suggest products to customers all benefit from these recommendation systems. The algorithms implemented in this research train their models on the MovieLens dataset and provide users with tailored movie recommendations. The study compares different machine learning algorithms, which include a Content-based model, item-item and user-user collaborative filtering (CF), Collaborative filtering with Singular Value Decomposition (SVD), K Nearest Neighbors, and Non-negative Factorization. The algorithms are evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure their accuracy and performance. While the proposed system which is based on a collaborative approach using SVD determines the connection between various users and, depending on their ratings, recommends movies to others with similar tastes, subsequently allowing users to explore more. The proposed approach using collaborative filtering with SVD performs better with a minimal RMSE of 0. 880258 by giving accurate and appropriate recommendations to the user. The model is further evaluated using performance metrics like Precision, Recall, and f1 score. So, CF with the SVD recommendation model is chosen for implementation and is integrated into a web application that allows the platform users to rate and review the available digital content as well as allows them to restrict screen time using a parental control system. The results of the study in this paper are presented in the form of tables, graphs, and statistical analyses, and can be used to guide the development of new and improved recommendation algorithms.