Intelligent valorization: Integrating AI, imaging, and sustainability for next-generation biofuel systems

The accelerating global push toward sustainable energy has positioned biofuels as a critical alternative to fossil fuels. This review presents a comprehensive synthesis of how AI is revolutionizing biomass valorization and enabling more efficient, adaptive, and sustainable biofuel production. From early-stage crop monitoring to real-time process optimization, AI enhances every phase of the biofuel value chain. Techniques such as HSI, NIR, thermal imaging, X-ray CT, and 3D-LS—when integrated with ML models like XGBoost, CatBoost, CNNs, and LSTMs—enable precise feedstock characterization, composition analysis, and dynamic control of pretreatment and conversion parameters. Special emphasis is placed on AI-enhanced preservation strategies including torrefaction, drying, ozone treatment, ACP, and PEF treatment. AI also supports advanced MOO in biochemical and thermochemical pathways, including hybrid models (e.g., GA-ANN, PSO-SVR, ACO-clustering) that maximize yields and co-product value. Furthermore, the review explores AI’s role in enabling circular bioeconomy models—linking biomass with plastic waste streams, supporting co-product valorization into biochar and bioplastics, and informing carbon market integration. Recent bibliometric trends and validation frameworks—including PCA, RMSE, MAE, and feedback loops—are also reviewed to ensure transparency, scalability, and real-world readiness of AI-driven systems. Finally, the computational footprint of AI is critically examined in light of LCA and TEA frameworks. By unifying technical innovation with sustainability considerations, this review offers a roadmap for researchers, industry stakeholders, and policymakers to harness AI for next-generation biofuel systems.