Machine Learning (ML) and its transformative role in advancing bioenergy technologies. As the global need for sustainable energy solutions intensifies, bioenergy stands out as a renewable resource with immense potential. However, its development is often hindered by the complexity and variability of biomass materials and conversion processes. Machine learning addresses these challenges by leveraging large datasets to predict outcomes, optimize operating conditions, and uncover hidden patterns. This chapter provides an overview of how ML techniques, such as Random Forest, Neural Networks, and Extreme Gradient Boosting, are applied to enhance bioenergy processes like hydrothermal carbonization, pyrolysis, and biogas reforming. It sets the stage for the following chapters, which explore detailed applications and case studies, illustrating how ML drives innovation and efficiency in the quest for sustainable energy.