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, proteins, 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 Natural 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 integrated. 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.