The ready-made garment (RMG) business has a huge role in manufacturing and economic growth, especially in developing countries like Bangladesh. Nevertheless, it is still struggling with issues that include dependence on a manual workforce, lack of uniform product quality, and supply chain operations that are not efficient. An opportunity to overcome these problems by introducing Industry 4.0 technologies is transformative in nature, as it allows automating and making decisions based on facts. The current paper provides a review of the implementation of Machine learning (ML) throughout the whole cycle of manufacturing in RMG. This paper is a systematic investigation of the application of ML methods, such as Convolutional Neural Networks (CNNs), predictive analytics, and generative models to various major production processes, such as procurement of raw materials and inspection of fabrics, pattern making, cutting, sewing, printing, finishing, and final quality control. Furthermore, the paper examines how ML can be used in combination with other Industry 4.0 enablers such as the Internet of Things (IoT) and Big Data analytics to improve demand prediction, logistics, and retail feedback analysis. Although the advantages of ML in the RMG sector are promising, the implementation of ML is still piecemeal. The review summarizes the existing literature, outlines the existing gaps, and provides some of the recommendations to be made in the future in a bid to create the shift toward an intelligent, sound, and sustainable garment industry.