AI-Driven Nutritional Intelligence: A Predictive Data Analytics Framework for Personalized Dietary Optimization

Public health is critically dependent on nutrition, yet malnutrition and poor dietary habits persist globally. This study presents an AI-driven nutritional intelligence framework that leverages machine learning, behavioral insights, and real-time data analytics to enhance nutritional well-being. By integrating heterogeneous datasets-including dietary surveys, medical records, socioeconomic profiles, a nd g eospatial data-the proposed model identifies key nutritional deficiencies and risk factors for diet-related diseases. Predictive modeling is employed to generate personalized dietary recommendations, while wearable and mobile technologies facilitate real-time nutrient monitoring. Additionally, AI-powered learning platforms and community-driven interventions improve adherence to healthier eating habits. The study evaluates the framework through case studies and pilot programs, demonstrating measurable improvements in dietary outcomes and reduced nutritional disparities.