Machine learning (ML) and artificial intelligence (AI) have become popular in modern industries because they can transform traditional industrial practices through data-driven decision-making, pattern recognition, and predictive modeling. In textile surface modification, ML and AI are now widely used to optimize processes such as dyeing, printing, coating, and finishing. These essential tools can leverage an extensive database, optimize process parameters, and reduce human error. This chapter provides information on ML and AI models, including support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), genetic algorithms (GAs), and fuzzy logic. They are incorporated with surface treatments to predict variables such as dye uptake, fabric strength, and moisture retention. Predicting critical parameters like temperature, time, and chemical concentration is a crucial part of surface-modification treatments. ML algorithms make the treatment process advanced by creating complex relationships between critical input parameters and consistent outputs. These technologies are used to reduce resource consumption and waste generation. Moreover, ML and AI models significantly improve the inspection system by identifying defects. Wrinkles, stains, discoloration, and uneven coating cannot be detected accurately by traditional human scanning systems. Despite its potential, many challenges and limitations exist, including the complexity of textile materials, limited data, and the computational intensity of deep learning approaches. However, the prospect of AI in textile surface modification is bound to continue revolutionizing manufacturing, bringing greener, more innovative, and more efficient processes.