This study aims to examine how artificial intelligence (AI) technologies contribute to sustainability and circular economy transitions in the textile and apparel sector by identifying technological pathways that enable eco-efficiency, waste reduction and circular material recovery across the textile value chain.
A systematic literature review was conducted using the Web of Science database to identify studies applying AI methods – such as machine learning, deep learning, computer vision and optimization – in sustainability- and circularity-oriented textile applications. Bibliometric mapping, keyword cooccurrence analysis and thematic synthesis were used to identify dominant research trajectories and conceptual clusters.
Three dominant research trajectories emerged: (1) AI for sustainable textile production and process optimization; (2) AI for circular textile waste management, automated sorting and wastewater treatment; and (3) AI for textile engineering and quality control. The findings show that AI mainly supports localized eco-efficiency improvements, including resource savings, defect reduction, improved material classification and enhanced operational performance. However, explicit life-cycle assessment, social sustainability evaluation and system-level circularity measurement remain limited. Research activity is also geographically concentrated in technologically advanced textile regions.
This review integrates fragmented research streams and identifies future research directions linking AI performance with verifiable sustainability and circularity indicators.
