Recommender systems filter user preferences and surfing history to provide recommendations. These recommendations are used to capture the user interests for making decisions. Based on the interaction of like, and dislikes of the user the decisions are made. We are using the deep learning techniques to enhance the movie recommendations. It has the ability to extract meaningful patterns from large volumes of data. This study uses Artificial Neural Networks (ANN) to learn features from user behavior and movie metadata, and Recurrent Neural Networks (RNN) to capture temporal patterns in user preferences and thereby enhance the recommendation accuracy by considering both short-term and long-term factors. Additionally, Convolutional Neural Networks (CNN) enhance model capabilities by focusing on input data spatial correlations. By combining CNN’s ability to extract hierarchical representations of structural and visual aspects into our recommendation system, we intend to improve material knowledge. These techniques play a vital role in providing recommendations by enabling the personalized preferences to the users. The models are trained on diverse datasets using user ratings and viewing history. Model performance on datasets shows decreased mean square and mean absolute error. This research shows how ANN, RNN, and CNN algorithms can provide reliable movie suggestions.