The shift toward digital news consumption has highlighted the growing need for personalized news recommendation systems. This study explores the development of a news recommendation system grounded in deep learning methods, using the extensive “Microsoft News Dataset (MIND).” The MIND dataset offers a comprehensive collection of user interactions and news content data. To capture the intricate relationships between users and articles, we employ advanced deep learning models, such as neural collaborative filtering and natural language processing techniques. The system incorporates user click history, article metadata, and contextual details to deliver personalized recommendations. Our experimental findings show notable improvements in recommendation precision and user engagement compared to traditional collaborative filtering methods. This research underscores the potential of deep learning to enhance both the relevance and diversity of news recommendations, fostering a more tailored and engaging digital news experience.