The integration of Artificial Intelligence (AI) and Knowledge Management (KM) in healthcare is transforming decision-making, resource allocation, and service efficiency, aligning with the United Nations’ Sustainable Development Goals (SDGs). AI-driven KM systems enhance clinical decision-making, optimize disease detection, and expand medical access, particularly in underserved areas, supporting SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities). Additionally, AI contributes to SDG 4 (Quality Education) by improving medical training and SDG 13 (Climate Action) through energy-efficient healthcare solutions. Despite its potential, the adoption of AI in healthcare faces significant challenges, including data privacy concerns, algorithmic biases, and regulatory uncertainties. The lack of comprehensive studies assessing AI’s measurable impact on sustainability further limits its large-scale implementation. This study addresses these gaps by analyzing how AI-driven KM systems support SDGs and identifying key obstacles to their integration. The research conducts a bibliometric and thematic analysis and examines 1,095 peer-reviewed papers from the Scopus database. Findings highlight AI’s role in enhancing efficiency, enabling knowledge-sharing, and improving resilience in healthcare systems. However, concerns regarding ethical governance, equitable access, and technological disparities underline the necessity of strong policy frameworks to ensure responsible AI deployment. This study contributes to the discourse on AI’s role in healthcare by providing a structured analysis of its impact on SDG achievement. Finally, the article addresses future research perspectives as joint scholars’ and practitioners’ analyses.