Supply chain management plays a critical role in the process of converting raw materials into final products and services. Although, conventional supply chains may fall short of the modern expectations of efficiency, cost-efficiency and sustainability. Green Agri-Food Supply Chain Networks (GASCN) are facing high transportation cost, long-time delivery, and inefficient resource utilization, which this study aims to address. In light of these challenges, this study presents a new Enhanced Search Spring Algorithm (ESSA), which optimizes GASCN by minimizing total transportation costs and is characterized by improved solution quality and computational efficiency. The ESSA algorithm integrates a non-linear adaptive weight factor and chaotic map strategies to lay a foundation for balancing the exploration or exploitation of the transmitted solution, allowing it to outpace existing metaheuristic algorithms. The results show that ESSA is superior to state-of-the-art algorithms. In the benchmark tests, ESSA gained much lower mean fitness values (61% lower than GA and 59% lower than PSO) and execution times (32% lower than PSO execution time). For example, in a real-world case study, ESSA minimized transportation cost at 40,000.13 units lower than GA (77,152.08 units) and PSO (77,025.35 units). Moreover, ESSA provides a stable scalability with time complexity proportional to linear growth with increasing problem size. This solution provides an efficient model for agricultural supply chain framework optimization, to a great extent attain quicker conclusions and effective asset appropriation. An important transformation from the time-tested approaches taking us toward sustainable and efficient agri-food systems, ESSA patterns focus on the urgency of these critical problems: cost reductions and operational inefficiencies.