Waste valorization is a key approach for transitioning from linear resource use to a sustainable circular bioeconomy. Recent advancements include new processes for integrating and optimizing bioprocessing alongside thermochemical conversion pathways. This review covers how artificial intelligence (AI) and machine learning (ML) impact system-level sustainability. It discusses major biological and thermochemical methods—such as pyrolysis, gasification, and hydrothermal processes—for converting municipal, industrial, agricultural, and food wastes into bioenergy, biofuels, biochar, and valuable bioproducts. The review also examines AI/ML applications in feedstock analysis, process modeling, yield forecasting, and operational optimization, highlighting their roles in enhancing efficiency and reducing environmental impacts. Special emphasis is placed on AI-assisted integration within hybrid biorefinery platforms, optimizing operating conditions, and utilizing digital twins and soft sensors for real-time monitoring and control. Furthermore, it explores combining AI-driven process models with life cycle assessment, techno-economic analysis, and multi-criteria decision-making to support sustainable process design and policymaking. Challenges related to data quality, model robustness, interpretability, and industrial adoption are also discussed. Overall, the review underscores that AI-driven process integration and optimization can significantly improve the sustainability, scalability, and economic viability of waste valorization systems. • AI integrates bioprocessing and thermochemical routes for circular bioeconomy systems. • Waste streams become valuable feedstocks for bioenergy, biofuels, and biochar. • Machine learning improves yield prediction, process control, and efficiency. • Hybrid biorefineries enable cascading use of waste with higher resource recovery. • AI-linked LCA and TEA support sustainable design and policy decisions.
Prabhu et al. (Sun,) studied this question.