Journal of Intelligent Learning Systems and Applications, 2025, 17(3), 133-148 https://www.scirp.org/journal/jilsa ISSN Online: 2150-8410 ISSN Print: 2150-8402 DOI: 10.4236/jilsa.2025.173010 Jul. 31, 2025 133 Journal of Intelligent Learning Systems and Applications Optimizing Medical Supply Chain Resilience for Future Pandemics: A Data-Driven Framework Integrating Public Health Risk, Logistics Efficiency, and Predictive Analytics Mst. Hasna Akter 1 , Soumitra Palit 2 , Kazi Md. Shahadat Hossain 3 , Md. Ekramul Hoque 4 , Tahera Shabnam 3 1 Department of Health Professions, Central Michigan University, Mount Pleasant, MI, USA 2 Department of Population Health (PHD), University of New Mexico, Albuquerque, NM, USA 3 College of Business Administration, Central Michigan University, Mount Pleasant, MI, USA 4 Department of Ketner School of Business, Trine University, Angola, IN, USA Abstract The COVID-19 pandemic exposed critical vulnerabilities in global medical supply chains, resulting in widespread shortages of essential healthcare prod- ucts. This study aims to optimize the resilience of medical supply chains against future pandemics by evaluating key mitigation strategies such as inventory buffering, multi-sourcing, and local manufacturing. Using an agent-based sim- ulation model that incorporates realistic demand surges, supplier disruptions, and transportation delays, we quantify the impact of these strategies on supply chain performance metrics including fulfillment rate, lead time, and recovery speed. Our results demonstrate that integrating multiple resilience strategies significantly improves service levels and reduces recovery times compared to traditional supply chain configurations. The findings provide actionable in- sights for policymakers and healthcare organizations to strengthen prepared- ness and ensure reliable access to critical medical supplies in times of crisis. Keywords COVID-19, Medical Supply, Health Risk, Supply Chain 1. Introduction The COVID-19 pandemic crisis was a first-time global stress test for health care How to cite this paper: Akter, M.H., Palit, S., Hossain, K.M.S., Hoque, M.E. and Shab- nam, T. (2025) Optimizing Medical Supply Chain Resilience for Future Pandemics: A Data-Driven Framework Integrating Public Health Risk, Logistics Efficiency, and Pre- dictive Analytics. Journal of Intelligent Learn- ing Systems and Applications, 17, 133-148. https://doi.org/10.4236/jilsa.2025.173010 Received: July 4, 2025 Accepted: July 28, 2025 Published: July 31, 2025 Copyright © 2025 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access