Optimizing vaccine distribution with machine learning: Enhancing efficiency, equity, and resilience in public health supply chains

The COVID-19 pandemic revealed significant issues with current vaccine delivery systems and highlighted potential improvements. Pharmaceutical companies rapidly developed and produced effective vaccines; however, distributing these doses fairly and promptly proved challenging. Traditional distribution methods often relied on fixed planning models and reactive logistics, which struggled to adapt to sudden changes in demand or disruptions in the supply chain. Consequently, machine learning (ML) has emerged as a transformative tool for enhancing vaccine distribution processes. This study explores how ML methodologies—such as supervised learning for demand forecasting, reinforcement learning for adaptive resource allocation, and unsupervised clustering for population segmentation—can improve distribution pipelines. A comprehensive review of thirty peer-reviewed studies indicates that ML techniques can promote equity, accelerate delivery, and minimize waste. Simulation models demonstrate that ML-based allocation systems can reduce vaccine waste by 27%, improve regional equity by 33%, and decrease delivery delays by 21% compared to traditional systems. Beyond technological advantages, ML enables policymakers to prioritize vulnerable populations or low-income areas by incorporating social justice considerations into optimization models. Nonetheless, challenges remain, including algorithmic bias, data privacy concerns, and insufficient digital infrastructure in resource-limited regions. The study's findings suggest that integrating ML into governance frameworks—characterized by transparency, fairness, and adequate funding—can significantly enhance immunization campaign effectiveness. These insights offer practical guidance for implementing ML solutions in routine vaccination efforts and pandemic preparedness, benefiting governments, healthcare organizations, and technologists alike.