AI-Driven Transformation in the Textile Industry: A Bibliometric Analysis and Scoping Review

Abstract

Artificial Intelligence (AI) is rapidly reshaping the global textile industry, driving efficiency, precision, and sustainability across its value chain. Yet despite growing enthusiasm, the integration of AI remains fragmented, with limited statistical understanding of where, how, and why these technologies take root. This study addresses that gap by combining bibliometric network analysis and systematic scoping review to map and statistically interpret two decades (2003–2023) of research on AI applications in textiles. Using association strength normalization, VOS modularity clustering, and thematic centrality density mapping, we identified eight manufacturing clusters ranging from fabric defect detection and supply chain optimization to textile waste management and sustainability that structure the field. The novelty of this work lies in repositioning bibliometric analysis as a statistical instrument, not merely a descriptive tool. Keyword co-occurrence networks and citation trajectories are translated into evidence-based research agendas, connecting cluster signals to methodological pathways such as regression modeling, support vector machines, neural networks, and hybrid ML-statistical frameworks. This statistical logic is used to surface gaps. Particularly in empirical validation, predictive modeling, and cross-cluster integration and to chart future directions for data-driven textile innovation. By grounding future agendas in measurable statistical patterns rather than narrative interpretation alone, this study offers a rigorous analytical framework that links research structure to methodological opportunity. The resulting roadmap invites scholars and practitioners to bridge AI, textile engineering, and applied statistics, shifting the field from fragmented experimentation toward coherent, evidence-based innovation.