Abstract: This paper systematically reviews the pivotal role of artificial intelligence (AI) in driving the modernization of traditional Chinese medicine, using the transition from an experience-based paradigm to a data-intelligent paradigm as the central thread. This transition should not be viewed as a simple replacement of traditional experience, but as a process through which experiential knowledge is structured, complex relationships become computationally tractable, and research conclusions become more verifiable. Focusing on four key domains-material basis and mechanism elucidation, intelligent prescription optimization, pharmaceutical engineering and industrial transformation, and knowledge engineering with large language models—this study analyzes how AI reshapes traditional Chinese medicine research paradigms through data-driven modeling, multimodal integration, and system-level reasoning. The findings indicate that AI is accelerating the transition of traditional Chinese medicine from experience-driven practice toward data-driven, evidence-constrained, and human-in-the-loop decision support, improving quantification and interpretability in mechanism analysis, prescription design, and quality control. However, the maturity of AI applications varies across different domains, and challenges remain in model interpretability, knowledge standardization, and ethical governance. Future research should focus on developing multimodal and multi-scale integrated intelligent models, as well as establishing a coordinated framework that aligns technological advancement with ethical considerations, in order to improve the reliability and verifiability of AI-enabled traditional Chinese medicine research.