Lignocellulose‐Derived Energy Materials and Chemicals: A Review on Synthesis Pathways and Machine Learning Applications

Abstract Lignocellulose biomass, Earth's most abundant renewable resource, is crucial for sustainable production of high–value chemicals and bioengineered materials, especially for energy storage. Efficient pretreatment is vital to boost lignocellulose conversion to bioenergy and biomaterials, cut costs, and broaden its energy–sector applications. Machine learning (ML) has become a key tool in this field, optimizing pretreatment processes, improving decision‐making, and driving innovation in lignocellulose valorization for energy storage. This review explores main pretreatment strategies – physical, chemical, physicochemical, biological, and integrated methods – evaluating their pros and cons for energy storage. It also stresses ML's role in refining these processes, supported by case studies showing its effectiveness. The review examines challenges and opportunities of integrating ML into lignocellulose pretreatment for energy storage, underlining pretreatment's importance in unlocking lignocellulose's full potential. By blending process knowledge with advanced computational techniques, this work aims to spur progress toward a sustainable, circular bioeconomy, particularly in energy storage solutions.