Deploying advanced bioenergy techniques, along with carbon capture and utilization technologies, facilitates achieving negative emissions at a reduced cost. Global climate goals can also be attained using sustainably harvested biomass to satisfy the energy demand. Effective utilization of biomass resources necessitates the identification of a suitable bioenergy pathway based on the characteristics of the feedstocks. Moreover, the complex relationship between various parameters influencing the efficiency of bioenergy technologies must be revealed to ensure optimal usage of biomass resources. Adopting traditional modeling techniques to solve these intricacies associated with bioenergy systems requires enormous research resources, labor force, financial investment, and time. The limitations of the traditional methods can be overcome by employing artificial intelligence and a machine learning (ML)-based data-driven approach. This chapter summarizes common challenges in biomass feedstock detection, characterization, and factors influencing bioenergy processes and highlights how AL and ML models can address the current challenges to foster innovation for efficient and sustainable biomass management.