Traditional Chinese medicine (TCM), rooted in holistic philosophy, features a “multicomponent, multitarget, multipathway” therapeutic model that has long posed challenges for modern scientific interpretation due to its inherent complexity. Studies elucidating its biological mechanisms have historically relied on correlation-based analytical paradigms. Although artificial intelligence (AI) has been increasingly introduced into TCM research, most current applications remain confined to disease classification, outcome prediction, or herb–target association mining, with limited capacity to reconstruct the underlying biological logic of Zheng (TCM Syndrome) differentiation and formula compatibility. This review systematically elaborates on how network pharmacology serves as a foundational framework, constructing “herb–compound–target–disease” networks that align with TCM’s holistic nature, while AI addresses network pharmacology’s limitations—machine learning streamlines active component screening and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and deep learning decodes spectroscopic data, complex biological interaction networks, and formula synergies. The integrated “computational prediction-experimental validation” workflow, validated across oncology, metabolic diseases, and infectious diseases, has become the gold standard for mechanistic research. Additionally, AI revolutionizes TCM quality control by linking chemical signatures to stable efficacy, integrates multiomics data to construct holistic regulatory networks, and enables translational progress through precision patient stratification, real-world evidence integration, and TCM knowledge graphs that structure fragmented knowledge. With the advancement of technology, generative AI for drug design, large language models for mining ancient texts, and multimodal “life models” promise to deepen integration. Ultimately, AI transcends being a mere tool, translating TCM’s holistic philosophy into modern scientific language, advancing its modernization and internationalization, and offering insights for multitarget drug development in global healthcare.

Background: Acute Lung Injury/Acute Respiratory Distress Syndrome (ALI/ARDS) is a fatal inflammatory disorder driven by M1 macrophages and the associated inflammatory cascade. Targeted drug delivery to these cells is a promising therapeutic strategy. Methods: L-arginine was conjugated to chitosan of different molecular weights. The resulting curcumin nanocrystals (Arg-CS-Cur) were characterized for conjugation efficiency, zeta potential, stability, and drug release profile. Cellular uptake mechanisms and mitochondrial targeting were investigated in lipopolysaccharide (LPS)-induced M1 macrophages using specific endocytic inhibitors and confocal microscopy. Results: Low-molecular-weight chitosan (MW 50 kDa) showed the highest L-Arg conjugation efficiency (22.31%). The optimized Arg-CS-Cur nanocrystals exhibited high zeta potential (±47.5 mV), excellent stability, and a superior drug release. They were internalized by M1 macrophages more efficiently than unmodified CS-Cur or free curcumin (p < 0.05). Uptake occurred via clathrin-mediated endocytosis (p < 0.001) and was mediated by CAT-2, which was highly expressed in M1 macrophages (p < 0.001). Arg-CS-Cur specifically targeted the mitochondria, reducing ROS and NLRP3 expression, thus inhibiting the NLRP3 inflammasome pathway (p < 0.001). Conclusions: This L-arginine-modified chitosan-based nanodelivery system synergistically exploits CAT-2 and clathrin pathways to deliver curcumin to M1 macrophage mitochondria, inhibiting the NLRP3 inflammasome. This dual-targeted strategy offers a promising approach for treating ALI/ARDS.