The increasing prevalence of obesity among children and adolescents poses significant public health challenges, necessitating innovative approaches for early prevention. This study proposes Ayur-Deep, an AI-powered personalized learning framework that integrates educational strategies with predictive analytics for obesity risk management in young populations. The framework leverages machine learning algorithms to identify individual risk profiles based on behavioral, dietary, and lifestyle data, while delivering adaptive educational interventions through interactive digital modules. By personalizing health education and prevention strategies, AyurDeep aims to enhance awareness, encourage positive lifestyle modifications, and support early intervention. The research further explores the integration of AI-driven learning analytics with evidence-based health guidelines to design scalable, youth-centric obesity prevention programs. The proposed framework contributes to advancing digital health education, fostering sustainable behavior change, and reducing the long-term burden of obesity in society.
AI-powered learning, personalized education, childhood obesity prevention, youth health, Ayur-Deep, predictive analytics, adaptive interventions, digital health education, machine learning in healthcare, early prevention strategies