Abstract

The increasing complexity of public health challenges—ranging from epidemic forecasting to resource allocation—necessitates the adoption of advanced data analytics and intelligent decision-support systems. This review explores the integration of Artificial Intelligence (AI) within Business Intelligence (BI) frameworks to support strategic decision-making in public health agencies. By harnessing AI capabilities such as machine learning, natural language processing, and predictive analytics, public health organizations can transition from reactive to proactive decision models. These AI-enhanced BI tools enable real-time data ingestion, automated anomaly detection, and adaptive modeling to uncover hidden trends and optimize health interventions. This paper examines the architectural design, implementation strategies, data governance requirements, and case studies demonstrating the value of AI-driven BI platforms in strengthening population health outcomes. Emphasis is placed on ethical considerations, interoperability standards, and the importance of explainable AI for ensuring transparency and accountability in government health operations. The review concludes by outlining best practices and future directions for embedding AI into public health analytics ecosystems.