Objective This study aims to systematically consolidate and analyze Professor Xiong Lei‘s clinical experience and patterns in utilizing aromatic traditional Chinese medicine(TCM) formulations for disease management. Methods Leveraging Professor Xiong Lei‘s outpatient case database spanning January 2023 to January 2024, this research rigorously screened 9 602 valid medical records through data cleaning, standardization, and augmented by an artificial intelligence(AI)-assisted model. The Apriori algorithm was applied to conduct frequent itemset mining and association rule analysis. Results High-frequency aromatic herbs identified included Ephedra sinica(processed), Xanthium sibiricum, Pogostemon cablin, and Bupleurum chinense. Notably, the herb pair "Ephedra sinica (processed) + Xanthium sibiricum" and the triplet combination "Ephedra sinica(processed) + Xanthium sibiricum + Peucedanum praeruptorum" exhibited significant correlations with symptoms such as cough, nasal congestion, and rhinorrhea. Furthermore, the novel formulation "Ephedra sinica(processed) + Xanthium sibiricum + Peucedanum praeruptorum + Houttuynia cordata" demonstrated promising potential in treating phlegm-heat syndrome. Conclusion The integration of AI-driven data analysis models facilitates the exploration of extensive case datasets, enabling the identification of frequently used pediatric aromatic TCM combinations and their corresponding symptomatic indications. This approach not only serves as a valuable reference for preserving the expertise of seasoned TCM practitioners but also provides evidence-based insights to inform clinical pharmacotherapy.