Customer satisfaction and engagement are two of the most important things companies look for to expand their businesses. Organisations have started using machine learning techniques to recommend items for users to try. If the user engages with the recommendation and likes it, they will come back to the company for more, which will then result in more revenue for the company. Movie recommendation systems have played a big part in the success of many multinational corporations in the entertainment industry. Previous works on movie recommendations have focussed on one of two primary Recommender system methods, namely Collaborative Filtering (CF) or Content-Based filtering (CBF). Both methods suffer from certain drawbacks, which sometimes hold them back from making meaningful recommendations. CF often faces challenges when dealing with sparse data, and CBF sometimes suffers due to over-reliance on user-defined preferences. One of the upcoming techniques widely being explored in recommender systems today is the usage of graphs or Graph Neural Networks (GNN) to make suggestions. This paper explores a method which plots the users as nodes of a graph, and each edge in the graph represents a common movie that the two users have rated. Then, a wide range of graph features are calculated and applied using GNN to make predictions and recommendations for particular user. Various evaluation metrics are then employed to test how well the model performs. This model is pretty advantageous as GNNs can understand user-item interactions and make good recommendations.