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The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases: exploration, based on migration simulation, and exploitation, based on hunting strategy simulation. To tackle the green lot size optimization issue, our methodology involved gathering real-world data, which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO emissions. This function served as input for the SOA model. Subsequently, the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability. Through extensive experimentation, we compared the performance of SOA with twelve established metaheuristic algorithms, consistently demonstrating that SOA outperformed the others. This study's contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma, thereby reducing environmental impact and enhancing supply chain efficiency. The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies, making it a promising approach for green manufacturing and sustainable supply chain management. [ABSTRACT FROM AUTHOR]