Abstract

The integration of Geographic Information Systems (GIS) with predictive modeling and machine learning is reshaping healthcare analytics by enabling spatially informed, data-driven decision-making. This chapter examines the evolution of GIS from a mapping tool to a comprehensive framework supporting disease surveillance, healthcare accessibility analysis, predictive modeling, and policy planning. It highlights the role of GIS in spatial feature extraction, modeling spatial dependencies, and visualizing complex health patterns to enhance anticipatory planning and resource allocation. The chapter also discusses the integration of machine learning and AI into GIS workflows to improve predictive accuracy and context-aware interventions. Key issues, including data privacy, ethical governance, interoperability, and model interpretability, are addressed. Emerging trends such as real-time monitoring, big data integration, precision public health, and participatory GIS demonstrate the potential of GIS-driven analytics to support equitable and resilient healthcare systems.