<p>Deep learning (DL) methods have opened unprecedented opportunities in the textile industry by providing critical insights into fibre-yarn-fabric defect detection, quality control, quality management, process automation and optimisation. The present work aims to summarise the literature related to DL applications in the textile industry and highlight future research agenda. This review combines bibliometric and network analyses of the relevant research papers. Content analysis was also done to elucidate various DL algorithms used at different stages of textile manufacturing and quality management. Convolutional neural network (CNN), deep neural network (DNN) and You Only Look Once (YOLO) are the most widely used DL techniques for fibre classification, fabric defect detection, garment classification, body shape reconstruction, etc. Some practical difficulties related to the applications of DL include nature of the data, size and scope of the data, selection of appropriate models, process uncertainty, etc. This research makes threefold contributions. First, mapping of different DL algorithms used in the textile industry; second, classifying the applications of DL based on the stages of textile manufacturing and quality management; and finally, identifying the challenges and themes for future research directions. This review provides a comprehensive reference for industries and academia on DL applications in the textile industry.</p>