Abstract: Image-based intelligent diagnosis represents a trending research direction in the field of tongue diagnosis in traditional Chinese medicine (TCM). In recent years, machine learning techniques, including convolutional neural networks (CNNs) and Transformers, have been widely used in the analysis of medical images, such as computed tomography (CT) and nuclear magnetic resonance imaging (MRI). These techniques have significantly enhanced the efficiency and accuracy of decision-making in TCM practices. Advanced artificial intelligence (AI) technologies have also provided new opportunities for the research and development of medical equipment and TCM tongue diagnosis, resulting in improved standardization and intelligence of the tongue diagnostic procedures. Although traditional image analysis methods could transform tongue images into scientific and analyzable data, recognizing and analyzing images that capture complicated tongue features such as tooth-marked tongue, tongue spots and prickles, fissured tongue, variations in coating thickness, tongue texture (curdy and greasy), and tongue presence (peeled coating) continues posing significant challenges in contemporary tongue diagnosis research. Therefore, the employment of machine learning techniques in the analysis of tongue shape and texture features as well as their applications in TCM diagnosis is the focus of this study. In the study, both traditional and deep learning image analysis techniques were summarized and analyzed to figure out their value in predicting disease risks by observing the tongue shapes and textures, aiming to open a new chapter for the development and application of AI in TCM tongue diagnosis research. In short, the combination of TCM tongue diagnosis and AI technologies, will not only enhance the scientific basis of tongue diagnosis but also improve its clinical applicability, thereby advancing the modernization of TCM diagnostic and therapeutic practices.