Healthcare supply chains are vital in delivering medical goods and products so that patients get the products on time, especially during events that may trigger such diseases as flu. The COVID-19 experiences reveal the weakness in these supply chain systems and hence the need for enhanced supply chain forecasting and future formulations. Thus, the proposed paper aims to bring to the discussion AI-driven end-to-end health SCA models aimed at enhancing the SCA of Healthcare facilities. Based on synthetic data, machine learning, and optimization algorithms, the goals of the suggested models are to predict the demand and optimize the logistic processes. The process is a process of data gathering, model building, and assessment of the Model’s proficiency by the use of parameters such as Mean Absolute Error. The case proves a high degree of accuracy in the demand forecast while also maintaining optimal costs in the supply chain. This paper focuses on the opportunity to use AI not only to improve the indexes that characterize the healthcare supply chain but also as a research agenda with implications for practice. Lastly, the study brings the argument on the application of AI solutions in supply management chains to bear by driving effectiveness in the delivery of health services in crises.