Ionic liquid solventsIonic liquid solvents (ILSs) have emerged as effective agents for processing biomass, resulting in cellulose-rich materialsCellulose-rich materials (CRMs) that can be utilized across various fields. However, accurately predicting the properties of CRMs and deciphering the intricate interactions within this system can be quite complex. In this chapter, we explore the application of machine learning algorithms to forecast CRM characteristics, with a particular emphasis on the cellulose enrichment factor and solid recovery. The Cellulose Enrichment FactorCellulose enrichment factor (CEF) is a measure of how effectively a pretreatment or processing method increases the relative concentration of cellulose in the material compared to its original state. Solid RecoverySolid recovery (SR) refers to the proportion of the solid material that remains after a pretreatment or processing step, relative to the initial amount of raw material. The machine learning algorithms (MLA) analyzed datasets comprising of 23 features, which encompass biomass properties, operational conditions, the specific ILS’s, and catalyst details. Among the various algorithms evaluated, the random forest model proved to be the most reliable, achieving a root mean square error (RMSE)of 0.22 and an R \(^{2}\) value of 0.94. This indicates a strong correlation between the predicted and actual values. Notably, biomass properties and ILS operating conditions were identified as the most influential factors, contributing 80% to the CEF and 60% to SR. To aid in visualizing and interpreting the relationships between these significant features and CRM properties, one-way and two-way partial dependence plots are presented. These findings potentially useful for designing ILSs or enhancing process efficiency.