Machine learning (ML) techniques can be used to predict the specific methane yieldsSpecific methane yields (SMY) of lignocellulosic biomassLignocellulosic biomass (LB) by analyzing a 14-feature dataset. These features cover both the characteristics of the biomass and the operating conditions of continuously fed, completely mixed reactors. Among the ML models tested, the random forest (RF) model performed the best, achieving a high level of predictive accuracy with a coefficient of determination (R \(^2\) ) of 0.85 and a root mean square error (RMSE) of 0.06. The composition of the biomass was found to have a significant impact on methane yields. Cellulose emerged as the most important factor, outweighing lignin and the biomass ratio in terms of influence. Using the RF model, the effect of the ratio of lignocellulosic biomass to manure was examined to determine the optimal conditions for biogas production. It was found that, under standard organic loading rates (OLR), a 1:1 ratio of lignocellulosic biomass to manure yielded the best results. Experimental tests confirmed these findings, with the highest SMY reaching 79.2% of the value predicted by the RF model. This chapter highlights how machine learning can be effectively applied to model and optimize anaerobic digestion processes, particularly when working with lignocellulosic biomass. By leveraging ML techniques, valuable insights are gained into the factors that influence methane production and improve the efficiency of biogas generation.