Purpose – The paper aims to provide an overview of the area of smart textiles. Design/methodology/approach – The paper describes and discusses new and developing materials and technologies used in the textile industries. Findings – Significant progress has been achieved in the area of technical textiles. Fibres, yarns, fabrics and other structures with added-value functionality have been successfully developed for technical and/or high performance end-uses. The basic building blocks are already in place in the field of smart textiles and clothing. Practical implications – As progress in science and engineering research advances, and as the gap between designers and scientists narrows, the area of smart clothing is likely to keep on expanding for the foreseeable future. Growth is predicted to occur in two distinct directions: performance-driven smart clothing and fashion-driven smart clothing. There are challenges that have to be addressed. Originality/value – The paper provides information of value to those interested in the future directions of the textile industry.

The contact angles, wicking properties, scourability, and dyeability of wool and cotton fabrics are affected by low-temperature plasma treatments. After treatment, wool and cotton fabric specimens show increased hydrophilicity and improved scouring and dyeing processing by nearly 50%.

Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness mechanical energy while enabling self‐sustaining sensing for self‐powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low‐power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI‐enhanced TENG solutions.