In recent years, the popularity of food recommendation systems has significantly increased due to the overwhelming amount of food-related information available online. These systems aid users in discovering new and personalized food choices, thereby enhancing their dining experiences. This paper presents a hybrid approach that combines content-based filtering (CBF) and collaborative filtering (CF) techniques to develop a robust and accurate food recommendation system. The proposed system leverages CBF to generate recommendations based on the intrinsic characteristics of food items, such as ingredients, cuisine, and nutritional content. By analyzing these attributes, the system identifies similarities between items and recommends new dishes that align with users' preferences. Additionally, the system incorporates CF to offer personalized recommendations based on user behavior and preferences. Collaborative filtering utilizes the collective knowledge of a user community to suggest food items that are popular among individuals with similar tastes. To implement the hybrid approach, a dataset containing food item details, user preferences, and ratings is collected and preprocessed. The content-based filtering component utilizes natural language processing techniques to extract relevant features from textual data, while the collaborative filtering component employs matrix factorization algorithms to identify user-item interactions and uncover latent factors. The system's performance is evaluated using standard evaluation metrics, including precision, recall, and mean average precision. The interface enables users to provide feedback and ratings on recommended food items, further refining the system's recommendations. The proposed food recommendation system holds great potential for improving user satisfaction and engagement in the culinary domain. By combining the strengths of content-based and collaborative filtering techniques, the system effectively captures both item characteristics and user preferences, resulting in more accurate and diverse food recommendations.