In this session, we will explore the transformative power of machine learning in optimizing sustainable energy production from agricultural waste. Specifically, we will examine how predictive analytics can be employed to forecast spatial biomass distribution, efficiently allocate resources, and design a cost-effective bioenergy supply chain. Using real-world examples, we’ll demonstrate how these technologies are vital in transitioning to a lower-carbon future, benefitting both the environment and farmers. This discussion promises to provide insightful, practical knowledge on employing machine learning for sustainability challenges in energy sector.