The growing emphasis on health and wellness has driven the need for precise and accessible dietary information. While food packaging labels offer essential nutritional insights, the manual interpretation of these labels can be time consuming and error prone, particularly for individuals with specific dietary needs. This paper introduces an advanced automated system designed to bridge this gap. By leveraging the EfficientDet model for object detection and PaddleOCR for text recognition, the system identifies and extracts key nutritional details from food labels, such as calories, macronutrients, and allergens. What sets this solution apart is its ability to provide personalized dietary recommendations tailored to user’s unique health profiles, including factors such as Body Mass Index (BMI), allergies, and chronic health conditions. By integrating user preferences, the system empowers individuals to make well informed food choices, fostering healthier eating habits. This work aims to enhance the accuracy, efficiency, and personalization of dietary guidance, ultimately promoting greater transparency in food labeling and supporting better health outcomes.