AI-Driven Textile Recycling Pathways and Sustainable Composite Design: A Systematic Review

Artificial Intelligence (AI) is being applied across textile-recycling systems to support material identification, automated sorting, process optimization, recovered-fiber assessment, sustainable composite design, and circular-economy decision-making. This systematic review synthesizes recent developments in AI-driven recycling pathways for sustainable textile composites. A structured Scopus search followed by predefined screening and eligibility assessment yielded 121 studies published between 2018 and 18 June 2026, and the review process was reported in accordance with the PRISMA 2020 framework. The reviewed evidence spans applications ranging from categorical fiber recognition to composition-aware and process-oriented approaches based on machine learning, deep learning, computer vision, spectroscopy, and optimization methods. Near-infrared, hyperspectral, Raman, and image-based systems have shown strong potential for identifying pure and blended textiles, while predictive models have been applied to estimate recovered-fiber quality, optimize recycling conditions, and predict mechanical and functional properties of recycled composites. Mechanical, chemical, and thermal recycling pathways exhibit different trade-offs in feedstock tolerance, material quality, process severity, and recovery potential, indicating that pathway selection remains context dependent. Life-cycle and circularity studies further suggest that higher recovery or recycled content does not necessarily correspond to lower environmental burden, particularly when energy use, substitution potential, material quality, and downstream recyclability are considered. Despite methodological advances, broader industrial implementation remains constrained by data representativeness, ground-truth quality, model transferability, explainability, and end-to-end system integration. Overall, the reviewed evidence suggests that further progress may depend on linking material characterization, quality-aware processing, composite-performance prediction, and sustainability assessment within interoperable and experimentally validated decision-support frameworks.