In this chapter, machine learning is employed to develop a model that predicts the yield of solid products from biomass torrefaction. The model uses input features that describe both the characteristics of the biomass and the conditions under which the torrefaction process occurs. Several machine learning algorithms were evaluated using ten-fold cross-validation, and their settings were fine-tuned through a detailed grid search. Among the tested algorithms, the gradient tree boosting method performed the best, achieving a prediction accuracy with an R \(^2\) value of nearly 0.90 and an average error of 0.07 w/w. Six features were identified as particularly important for the model’s predictions. For the torrefaction conditions, these included the temperature, the residence time, and the oxygen concentration in the reacting gas. For the biomass properties, the key features were the levels of volatile matter, carbon content, and oxygen content. Interestingly, all features except carbon content were found to decrease the yield of torrefied biomass. In terms of overall influence, the properties of the biomass contributed approximately 30% to the solid yield, with volatile matter alone making up about one-third of this contribution. This approach highlights how machine learning can effectively predict torrefaction outcomes by combining process conditions and biomass characteristics, offering valuable insights for optimizing the process.