Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) presents a paradigm shift for enhancing sustainability within the textile industry. This review examines the transformative potential of these technologies in fostering a circular economy, with a focus on material design, process optimisation, and end-of-life solutions. It surveys applications across textile science, from natural fibre composites to technical and smart textiles, highlighting the role of predictive modelling and ML algorithms—including neural networks, convolutional neural networks (CNNs), and random forests. These techniques are demonstrated to enhance the design of fibre-based materials, predict key properties such as tensile strength and thermal stability, and optimise manufacturing processes like dyeing and weaving. Furthermore, the review explores the significant contribution of computer vision to automated quality control, defect detection, and the assessment of garment condition for resale, thereby supporting circular business models. A central theme is the capacity of AI to drive sustainability by enabling zero-waste pattern design, improving colour prediction accuracy to reduce chemical waste, and advancing automated textile sorting for recycling. Despite this promising progress, the principal challenges identified are not algorithmic but systemic, relating to data scarcity, integration complexities, and the need for cross-sector collaboration. The review concludes by identifying critical future research directions, emphasising the need for robust, physics-informed models, the collaborative development of larger, more diverse datasets, and AI-driven Design for Disassembly (DfD) to fully realise AI's potential in creating a more innovative, efficient, and sustainable textile industry.