The global textile and fashion industry generates 92 million tons of waste annually and is responsible for 20% of industrial water pollution (Ellen MacArthur Foundation). As sustainability concerns rise, AI and ML are emerging as transformative tools to mitigate textile waste, optimize production efficiency, and drive sustainable innovation. The textile industry is one of the largest contributors to environmental pollution, responsible for excessive water consumption, chemical waste, and carbon emissions. AI and machine learning (ML) present innovative solutions to optimize manufacturing processes, enable textile recycling, and enhance supply chain transparency. This paper explores AI-driven approaches to sustainable textile production, analyzing key environmental and economic data to evaluate AI’s impact via AI-driven textile sorting, predictive analytics for demand forecasting, and AI-assisted material innovation which can significantly reduce waste and pollution. The study highlights under-researched areas, challenges as well as proposes future research directions.