This chapter explores how artificial intelligence (AI) can help low- and middle-income countries (LMICs) overcome chronic shortages of health workers, infrastructure, and funding while safeguarding equity. After outlining the scale of the challenge—including a projected shortfall of ten million health-care workers by 2030 and 15 million premature non-communicable-disease deaths each year, 85% of which occur in LMICs—it surveys emerging AI applications from radiology triage in Ecuador to mobile decision-support tools in sub-Saharan Africa. Economic models suggest substantial efficiency gains, yet real-world pilots reveal that algorithmic success hinges on three inter-locking ‘building blocks’: (1) high-quality, context-specific data strategy and management; (2) ethical governance and robust regulation covering privacy, accountability, and bias; and (3) health-system integration backed by reliable connectivity, digital literacy, and sustainable funding. Case studies such as a racially biased breast-cancer histology model and South Africa’s Vumacam surveillance network illustrate how ungeneralisable datasets and opaque supply chains can reproduce or intensify existing inequalities. The chapter concludes with a practical roadmap for policymakers and implementers—emphasising interoperable data standards, participatory design, continuous monitoring, and cross-sector partnerships—arguing that AI is not a panacea but, when deployed deliberately, can accelerate progress toward Universal Health Coverage and the Sustainable Development Goals without leaving vulnerable populations behind.