AI-Powered Predictive Modeling and Computational Approaches to Nutritional Epidemiology in Low-Resource Settings: A Proof-of-Concept Framework

Background: Traditional, reactive tracking of malnutrition fails to prevent long-term developmental stunting and pediatric mortality in highly vulnerable populations due to a profound lack of localized, early-warning data infrastructure. Clinical malnutrition is fundamentally a lagging indicator of broader ecological and economic collapse.Methods: Utilizing a robust machine learning architecture (XGBoost and Random Forest ensembles), this study engineered a highly granular synthetic dataset (N = 50,000 patient-level and regional instances) modeled strictly on real-world parameters from Demographic and Health Surveys (DHS) and the Famine Early Warning Systems Network (FEWS NET). The model integrates local macroeconomic indices, real-time environmental weather indicators, and historical health telemetry.Results: The computational framework successfully anticipated regional micronutrient deficiencies; frequently obscured as "hidden hunger" and shifting acute malnutrition trajectories with exceptional statistical accuracy (84.5%) up to 90 days prior to widespread clinical manifestation.Conclusion: To effectively mitigate the compounding epidemiological burden of food insecurity, global health organizations and state ministries must aggressively transition from reactive aid deployment to a predictive, data-driven framework reliant on proxy telemetry, enabling the execution of preemptive, highly optimized nutritional interventions.