Using the theoretical framework of the Diffusion of Innovations Theory, this study investigates the integration of artificial intelligence (AI) into decision support systems in health care centers in Malaysia (MY). In order to investigate the significant correlations between system complexity, data quality, organizational readiness, user engagement, technological infrastructure, and the effectiveness of AI-powered decision-making systems in MY health care centers, this study uses a cross-sectional survey design. Purposive sampling is used to choose a representative sample of institutions according to factors including size, location, and academic reputation. Within each institution, participants are selected using stratified random sampling to guarantee department and job representation. To look at the relationships between the variables under investigation, structural equation modeling, or SEM, was utilized. System complexity did not show a significant association (β =  − 0.016, p-value = 0.65), but data quality, organizational readiness, user engagement, and technology infrastructure were important factors influencing effective decision-making processes with β = 0.503; 0.281; 0.193; and 0.244 at p-value less than.05, respectively. The recommendations state that the company should prepare, increase user interaction, improve technology infrastructure, and concentrate on investments in data quality assurance. This study offers useful insights for policymakers, healthcare professionals, and practitioners interested in leveraging AI to improve healthcare outcomes and foster innovation in healthcare facilities, despite its cross-sectional design and regional emphasis limitations.