Multimodal artificial intelligence (AI) is reshaping global health by integrating heterogeneous data streams, clinical records, medical imaging, sensor-based monitoring, geospatial information, and socioenvironmental determinants to generate equitable, context-aware healthcare solutions. This chapter examines how multimodal AI, combined with advances in geospatial intelligence and GIS, can bridge long-standing health equity gaps across resource-variable settings. Drawing on global evidence from Ghana, Bolivia, India, and Bangladesh, the chapter highlights three core mechanisms through which multimodal systems promote equity: early detection using wearables and edge computing, intelligent resource allocation integrating demographic and geospatial indicators, and personalized interventions powered by computer vision and culturally adaptive digital tools. The chapter further outlines the technical foundations of multimodal AI fusion architectures, transformer-based attention mechanisms, and emerging generative AI capabilities and maps their implications for public health, clinical decision-making, and universal health coverage. Key governance challenges such as algorithmic bias, data colonialism, transparency, digital divide, and evolving regulatory landscapes are examined in depth. Positioned within WHO’s global health equity agenda and the Sustainable Development Goals, the chapter argues that the convergence of multimodal AI, GIS, and precision population health represents a pivotal opportunity to democratize advanced healthcare, provided implementation is ethically grounded, culturally competent, and community-driven.