The U.S. healthcare supply chain constitutes an essential pillar of medical service delivery but faces persistent challenges that were severely exposed during the COVID-19 pandemic, including delayed response times, inefficient forecasting systems, and frequent stockouts. This research explores how Artificial Intelligence (AI), particularly predictive analytics and machine learning, can transform supply chain operations by shifting from reactive to proactive management. AI-powered models, such as Random Forest Regressors, were developed using synthetic datasets generated from historical and expert data to forecast demand and optimize distribution across various healthcare facilities. The integration of real-time data from IoT sensors and external market trends enables dynamic, responsive systems that can adjust rapidly to emergencies or regulatory changes. Key findings include a 40% improvement in forecasting accuracy, a 25% reduction in stockouts, and a 22% decrease in transportation costs, with AI models predicting crises 5–10 days earlier than traditional systems. The results also revealed that evaluation metrics like Mean Absolute Error (MAE), cost savings, and crisis response time (35% improvement) confirm AI’s superior performance. Notwithstanding an 85% prediction accuracy, challenges remain in data integration, stakeholder coordination, and the high upfront cost of AI infrastructure. Nonetheless, the findings demonstrate the significant value AI brings in enhancing resilience, transparency, and operational efficiency in healthcare logistics in the USA. The proactive AI system meets demand more accurately and ensures supply availability during crises, although avoiding excess inventory, thus providing a strong return on investment and laying the groundwork for sustainable, digitally transformed healthcare supply chains in the United States.