<p>Millions of preterm infants rely on human donor milk, which is collected, processed, and pooled by human milk banks to standardize its macronutrient content for safe consumption. Effective production and distribution planning in human milk banks are critical, particularly in developing countries where the network is still underdeveloped. Addressing the dynamic and uncertain nature of the human milk supply chain requires a flexible and adaptive approach. This study proposes a novel framework integrating data-driven robust optimization with a rolling horizon approach to manage supply and demand uncertainty and improve the network’s responsiveness. A distinctive overlap-controlled coverage model is introduced to ensure equitable access for infants across metropolitan areas. The framework is validated in collaboration with established human milk banks, utilizing weekly operational data streams to dynamically adapt to changing conditions. Results demonstrate that the proposed approach outperforms traditional deterministic models, improving adherence to clinical macronutrient targets by approximately 14.28% while enhancing the efficiency of milk production and distribution processes.</p>