Machine learning for lignocellulosic pretreatment and lignin valorization: a biomass-centered framework from process conditions to molecular design.

Lignocellulose sugar-platform biorefinery is regarded as a promising alternative to fossil-based refining, with biomass providing a renewable carbon resource for bio-based products. Lignocellulosic pretreatment remains a major bottleneck because process outcomes are governed by the coupled effects of biomass heterogeneity, solvent chemistry, and operating conditions. Machine learning (ML) offers a practical framework for learning structure-process-outcome relationships from sparse, heterogeneous, and experimentally costly datasets. This review interprets pretreatment as a continuum from operating-condition-dominated systems to molecular-design-oriented solvent systems, and examines how process conditions, chemistry, and biomass can be represented and learned. Further, ML applications are explored for predicting pretreatment outputs such as sugar recovery, delignification, and lignin structural features; identifying operating windows within fixed chemistries; comparing chemically distinct solvent or reagent systems when appropriate descriptors are available; and prioritizing new experiments under limited data. ML-enabled lignin valorization is further discussed through structural decoding, mechanistic modeling, structure-property mapping, and inverse design. Its future utility will depend more on how well ML matches the dominant sources of variation in pretreatment systems than on algorithmic complexity or novelty. By organizing ML around pretreatment and valorization problems rather than algorithm categories, this review provides a biomass-centered framework for more reliable and actionable data-driven biorefinery research.