<p>This study explores an optimization framework that robustly addresses the economic and sustainable production of second-generation biofuel from banana rachis, which used Genetic Algorithm for supply chain optimization. The work aims at finding solutions to the problem of global demand for renewable energy, especially by turning crop waste into banana to fuel bioethanol productions due to its being considered agricultural residue. This framework analyzes supply chain components such as sourcing, location of bio-refinery, and logistics to minimize the cost of production while maximizing environmental benefits. Banana rachis- it is a non-edible waste product, which proves to be an ecofriendly fuel alternative, with least amount of greenhouse gas release. The model integrates several parameters, including economic, environmental, and logistical, ensuring maximum scalability and adaptability for different geographical regions as well as production scales. The results show that, at the optimized supply chain level, the operational cost goes down and supports SDG. The current biofuel studies research has some gaps that this study tries to bridge, specifically targeting the less-researched area of banana rachis-based biofuel with a holistic approach toward its supply chain. This can add value to the policies, industry leaders, and researchers focused on developing renewable energy solutions.</p>