A Concordia University research team has developed an artificial intelligence-driven scheduling system designed to optimize operating room utilization and reduce surgical wait times. Led by Hossein Hashemi Doulabi, associate professor in the Department of Mechanical, Industrial and Aerospace Engineering, the system integrates room allocation, procedure timing, and case prioritization into a unified framework. By utilizing a significantly reduced set of mathematical variables compared to legacy approaches, the model processes complex weekly schedules with greater speed and scalability, making it viable for high-volume clinical environments. The platform operates on a dynamic, day-by-day replanning mechanism. This structure allows hospitals to accommodate unscheduled emergency procedures or sudden changes in patient acuity without triggering cascading disruptions. Testing across simulated environments and actual operational data from a clinical facility in Naples, Italy, demonstrated the system’s capacity to absorb same-day emergency arrivals with minimal deviation from baseline schedules. When adjustments were necessary, the algorithm leveraged targeted overtime, strategic room expansion, and the precise deferral of a limited number of elective procedures to maintain operational continuity. Published in the International Journal of Production Research, the research underscores the technology’s potential to streamline hospital logistics and improve resource management. The development team projects that widespread deployment could directly reduce day-of-surgery cancellations, compress surgical waitlists, and enhance financial oversight through optimized staff and equipment utilization. By automating complex scheduling decisions, the tool aims to provide clinical administrators with a reliable mechanism for maintaining system stability under fluctuating demand, ultimately supporting healthcare providers in delivering more consistent and timely patient care.