In this empirical research, we investigated the impact of artificial intelligence of things (AIoT) technologies on global healthcare effectiveness. Using the three-dimensional inter-influence correlations of social learning theory, we analyzed the AIoT’s technological scalability, situational effectiveness, and social adaptability across seven dimensions of the global healthcare index. A mixed-method approach combining factor analysis and three-dimensional methods was employed to evaluate the AIoT’s influence on worldwide healthcare challenges. Results indicate that intelligent medical analysis, medical monitoring and controlling applications, and comprehensive medical diagnosis systems have the strongest relationships with healthcare improvements, particularly in the skills and capabilities of medical personnel. These technological implementations showed the highest efficacy when aligned with the National Institutes of Health dimensions of sustainability, participation, and transparency. Additionally, significant impacts were observed in the access, distribution, and management of efficient healthcare services and adaptability to medical landscapes. In this research, we established a hierarchical framework for the AIoT implementation in healthcare settings, providing evidence-based guidance for policymakers and administrators seeking to leverage these technologies to enhance healthcare delivery systems globally. The findings suggest that the strategic implementation of AIoT solutions can systematically address critical challenges in contemporary healthcare provision across diverse global contexts.