AI-Optimized Resource Allocation Models for Public Health Emergency Response and Compliance Auditing

Abstract

this review explores the integration of artificial intelligence (AI) into resource allocation frameworks designed for public health emergency response and compliance auditing. The growing complexity of managing health crises—ranging from pandemics to natural disasters—demands real-time decision-making tools that are adaptive, scalable, and transparent. AI-driven models, including machine learning algorithms, optimization heuristics, and predictive analytics, have demonstrated significant potential in forecasting demand surges, allocating critical resources such as vaccines, medical staff, and equipment, and ensuring compliance with regulatory frameworks. This paper examines key AI techniques employed in dynamic prioritization, constraint-aware scheduling, and multi-agent coordination within health systems. Additionally, it reviews AIenabled auditing mechanisms that monitor compliance with safety protocols, ethical guidelines, and funding regulations during emergency deployments. By synthesizing current literature and case studies from global health responses, the review identifies best practices, technological gaps, and future research directions for building resilient and accountable public health infrastructures through intelligent resource management.