This chapter explores how machine learning can predict two important outcomes of hydrothermal liquefactionHydrothermal liquefaction (HTL)—biocrude yieldsBiocrude yields (BY) and energy content (measured as higher heating valueHigher heating value, HHV)—when processing wet biomass and waste materials. The study analyzed 17 different factors that could influence these outcomes, including the biological makeup of the feedstock and the processing conditions. Researchers tested several advanced machine learning methods, using a rigorous 10-fold cross-validation approach to ensure reliable results. Among these methods, the extreme gradient boosting (XGB) model proved most accurate. For biocrude yield predictions, it achieved an impressive 90% accuracy (R \(^2 = 0.9\) ) with minimal error (NRMSE \(= 0.16\) ). For energy content predictions, it maintained strong performance with 87% accuracy (R \(^2 = 0.87\) ) and similarly low error (NRMSE \(= 0.04\) ). The analysis revealed that process temperature was the single most important factor for both yield and energy predictions. However, the characteristics of the biomass feedstock itself accounted for more than half (55%) of the model’s predictive power. By examining how these different factors interact, the study provides new insights that can help optimize biomass conversion processes.