The textile industry is undergoing a transformation driven by Artificial Intelligence (AI) and Machine Learning (ML),progressing toward the "Fashion 4.0" paradigm. In this review, a systematic evaluation is carried out for AI and MLapplications across the textile lifecycle, fiber classification, yarn production, fabric formation, dyeing, printing, qualitycontrol, supply chain management, and sustainability. Drawing on peer-reviewed studies published between 2015 and2026, the review reports experimental performance benchmarks, such as convolutional neural networks (CNNs) achievingover 99% accuracy in fabric defect detection. Furthermore, ML-based dyeing optimization reduces water consumption andchemical usage. LSTM and Transformer-based models improve demand forecasting accuracy relative to statisticalbaselines. Persistent challenges include data scarcity, model interpretability, and integration with legacy systems. Thereview also identifies future research directions, including federated learning, digital twins, and foundation models.Overall, these findings indicate that AI and ML technologies can substantially enhance production efficiency, productquality, and environmental sustainability in the textile industry.