Abstract：In the theoretical framework of traditional Chinese medicine，the core structure of "syndrome-diseaseprescription- medicine" constitutes the foundation of treatment based on syndrome differentiation，as well as the basis of clinical reasoning and decision-making. With the continuous iteration and advancement of artificial intelligence （AI） technology in the field of human health, its application in traditional Chinese medicine has become increasingly widespread. Traditional Chinese medicine theoretical research and clinical practice can also leverage intelligent data analysis, offering new pathways for the development of traditional Chinese medicine theory. By systematically reviewing the application progress of AI in traditional Chinese medicine syndrome recognition, disease diagnosis, and prescription and medicine selection， this paper analyzes the specific practices of modern data processing technologies such as natural language processing, knowledge graphs, and deep learning in data mining and modeling. It also identifies the challenges facing the development of AI in traditional Chinese medicine，including the complexity of data structures, the strong polysemy of knowledge representation, and insufficient model interpretability. The paper emphasizes the need to strengthen the integration of multi-source heterogeneous data and to construct a standardized traditional Chinese medicine knowledge system, thereby enhancing the clinical adaptability and interpretability of AI models. From a theoretical perspective, this study aims to promote the deep integration of traditional Chinese medicine and AI, facilitating a new pattern for the development of intelligent traditional Chinese medicine.