AI is reshaping the future of healthcare supply chain management, increasing operational performance and reducing the level of wastage, to explore efficiency and cost benefits. This paper used quantitative forecast models, which have the potential to support sustainable economic growth of supply chain management. There are many issues and challenges that are associated with healthcare supply chains such as inventory control, demand forecasting, and resources. Management. These problems remain unsolved by traditional supply chain solutions leading to problems such as overstock, stockout, and wastage. AI technologies and applications like machine learning algorithms and predictive analytics offer solutions through their aptitude for forecasting, inventory management, and making the right decisions. Econometric techniques such as time series and econometric modeling quantitative techniques are used in assessing the economic implications of AI in healthcare supply chain innovation. These techniques involve studying past trends to forecast future behavior and these are beneficial in organizational demand changes, resource utilization, and general waste can be better estimated. Integrating AI with quantitative forecasting enables healthcare organizations to strengthen their operational resilience, adjust to changing market conditions, and realize cost savings. The study emphasizes several key advantages of AI adoption, including enhanced accuracy in demand forecasting, lower operational costs, and improved resource utilization efficiency. Furthermore, AI-powered tools assist organizations in managing uncertainties and responding proactively to disruptions, fostering overall economic stability and growth. Through utilizing quantitative forecasting methods, healthcare organizations can optimize their supply chain operations, promote sustainable economic growth, and improve service delivery.