AI POWERED DIET MONITORING PLATFORM USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING

The increasing incidence of lifestyle-related diseases such as obesity, diabetes, and cardiovascular disorders has created an urgent need for effective dietary monitoring and nutritional management. Proper nutrition plays a critical role in preventing and managing these diseases; however, existing digital diet-tracking applications often face significant challenges due to manual data entry, resulting in low user engagement and poor long-term adherence. This limitation, commonly known as manual entry fatigue, reduces the effectiveness of traditional nutritional monitoring systems. This research proposes an AI Powered Diet Monitoring Platform, an intelligent and user-friendly solution aimed at simplifying dietary tracking through automation and Artificial Intelligence. The system utilizes advanced Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to enable users to record their food intake using natural conversational language. Unlike conventional applications that require users to manually search for food items and input quantities, the proposed system interprets meal descriptions, identifies ingredients, estimates portion sizes, and computes nutritional values such as calories, proteins, carbohydrates, and fats in real-time. The platform integrates verified nutritional databases to improve the accuracy of nutrient estimation and applies clinical physiological models, including the Mifflin-St Jeor equation, for calculating personalized health metrics such as Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE). These metrics help users understand their energy requirements and support personalized diet planning. The development methodology follows an Agile framework, enabling iterative improvements in the AI model’s semantic understanding and system performance. The platform architecture is based on a scalable full-stack development model, ensuring secure data management, high performance, and future extensibility. Experimental evaluation demonstrates that the proposed system achieves an ingredient recognition accuracy of over 92%, significantly reducing user effort and increasing consistency in dietary logging compared to traditional methods. The study concludes that Artificial Intelligence has the potential to revolutionize diet monitoring by transforming it into an automated, intelligent, and insightful process. The proposed platform establishes a strong foundation for personalized digital healthcare systems and contributes toward long-term behavioral change, improved nutritional awareness, and prevention of chronic lifestyle diseases.