Artificial Intelligence for Sustainable Healthcare: A Systematic Review of AI-Driven Health Systems in Alignment with Sustainable Development Goals

Background: Healthcare systems globally face unprecedented pressures from population growth, escalating chronic disease burden, resource constraints, climate change, and health inequities. Artificial Intelligence (AI) presents a paradigm shift in addressing these challenges while promoting sustainability across environmental, economic, and social dimensions. Objective: This systematic review examines the role of AI in advancing sustainable healthcare delivery and identifies mechanisms through which AI-driven interventions contribute to achieving the United Nations Sustainable Development Goals (SDGs). Methods: A comprehensive literature review following PRISMA guidelines was conducted. Peer-reviewed articles, systematic reviews, meta-analyses, policy documents, and technical reports published between 2018 and 2025 were systematically analyzed. Search databases included PubMed, Web of Science, Scopus, and Google Scholar using combinations of keywords: "artificial intelligence," "machine learning," "sustainable healthcare," "health systems sustainability," "SDGs," "digital health," and "environmental health." Results: AI demonstrates significant potential across three sustainability dimensions: (1) Environmental—through energy optimization, waste reduction, and virtual care delivery; (2) Economic—via cost containment, resource efficiency, and operational automation; and (3) Social—by enhancing health equity, expanding access, and strengthening clinical decision-making. Evidence supports alignment of AI applications with five key SDGs: SDG 3 (Health and Well-being), SDG 8 (Economic Growth), SDG 9 (Industry and Infrastructure), SDG 12 (Responsible Consumption), and SDG 13 (Climate Action). Conclusion: AI integration into healthcare systems offers transformative potential for sustainability, contingent upon ethical implementation frameworks, robust data governance, healthcare workforce capacity building, and equitable access mechanisms. Future research must address implementation barriers, health equity concerns, and long-term sustainability metrics.