Traditional Chinese medicine (TCM) is a holistic medical system that classifies and treats diseases based on the concept of patterns. These patterns describe the pathophysiological process of a disease at a specific stage, reflecting both external signs and internal features. TCM patterns are central to pattern differentiation, treatment, clinical practice, and theoretical development in TCM. However, scientific explanation of TCM patterns remains limited because of subjective diagnostic criteria, the absence of a standardized experimental medical system, and unclear biological mechanisms, which restrict the modernization and globalization of TCM. Addressing challenges such as strong subjectivity in diagnosis, lack of standardized experimental systems, and unclear biological mechanisms is necessary to clarify the scientific meaning of TCM patterns and to provide technical approaches for the modernization and globalization of TCM. A strategy focused on “pathogenic factors, genetic predisposition, and disease progression stages” was adopted. This approach included the following: (Ⅰ) constructing disease-pattern integrated biological models with animal models, organoids, and multi-organ chips; (Ⅱ) applying multi-omics technologies, such as spatial omics, single-cell omics, and dynamic metabolic flux omics; (Ⅲ) using artificial intelligence (AI) and big data for data integration and prediction of pattern evolution; and (Ⅳ) validating formula–pattern associations through the “pattern differentiation through formula efficacy” approach. These strategies directly address the main obstacles in TCM pattern research by providing objective, quantifiable, and reproducible methodologies. Constructing disease-pattern integrated models enabled cross-scale research platforms. Applying multi-omics technologies allowed analysis of complex biological bases. AI and big data approaches addressed challenges related to heterogeneous data. The “formula-based pattern differentiation” approach supported precise interventions and the development of new drugs. This interdisciplinary framework advances TCM pattern research by moving from empirical description to objective quantification. By integrating innovative approaches, the study establishes a foundation for systematic, evidence-based TCM diagnosis and treatment, supporting accuracy and promoting international recognition and modernization of TCM. The study shows that combining multi-omics technologies, AI-driven data analysis, and disease-pattern models enables objective quantification of TCM patterns and clarifies their biological mechanisms.