Integrating ionic liquids and artificial intelligence for circular agri-food waste valorization

Background Agri-food waste is a major environmental burden but also a rich source of polyphenols, proteins, polysaccharides, and fibers. Conventional extraction methods often suffer from low selectivity, degradation of sensitive compounds, and high energy demand. Ionic liquids (ILs), with tunable structures, negligible volatility, and strong solvating capacity, have emerged as promising solvents for valorizing these residues. Scope and approach This review summarizes the key mechanisms underlying IL-assisted extraction, including hydrogen bonding, π-π stacking, hydrophobic/hydrophilic interactions, electrostatic interactions, and biomass disruption at the macro scale. It also examines process-intensified IL systems, such as ultrasound-, microwave-, enzymatic-, and aqueous two-phase-assisted extraction, which have achieved reported yield improvements of about 1.2- to 5.4-fold over conventional methods. In parallel, artificial intelligence (AI) tools, including machine learning, deep learning, Bayesian optimization, and generative models, are discussed as emerging approaches for IL screening, property prediction, and process optimization. Key findings and conclusions Integrating ILs with AI can shift IL-assisted extraction from empirical trial-and-error toward predictive solvent screening and process optimization, improving extraction efficiency, selectivity, and bioactive stability. However, AI should be viewed as a screening and decision-support tool rather than a direct solution to the intrinsic cost, recovery, toxicity, regulatory, and scale-up barriers of IL-based systems. Future development should integrate AI-assisted optimization with experimental validation, solvent-recycling studies, techno-economic analysis, life cycle assessment, and safety/regulatory evaluation to assess the realistic industrial potential of IL-assisted agri-food waste valorization.