Managing a nutritionally dense diet while having numerous health constraints like diabetes, bowel syndrome, etc., along with allergies can become a struggle keeping in mind the hassle a person goes through in their day-to-day life. The primary objective of the proposed work is to develop a one-of-a-kind meal planning tool that utilizes machine learning to produce customized diet plans based on the profile of each user. The system addresses the interconnected relationship between nutrition and nutrition-specific diseases, ensuring that the generated meal plans include adequate amounts of carbohydrates, fiber, pro-teins, fats, vitamins, and minerals. The nutrient constraints are set to the user’s profile by the dataset provided to the proposed system. The users are provided with a meal plan (breakfast, lunch, and dinner) using gradient boosting and nat-ural language processing. Unlike the existing personalized nutrition planners, the proposed framework offers diet recommendations based on user’s diseases in addition to allergies ensuring a tailored meal plan every time. The proposed system is set differently from the other reported framework as feedback is inte-grated. The users can give feedback by rating the diet plan from which the system improves resulting in a more optimal meal plan for the same similar profile users. The personalized diet recommendation system achieved 86% accuracy rate in predicting suitable meals with respect to user requirements.