This paper addresses the challenge of integrating Traditional Chinese Medicine (TCM) principles with contemporary artificial intelligence to generate accurate and personalized dietary recommendations. Focusing on the TCM concept of “One Root of Medicine and Food,” we develop a novel method that employs Retrieval-Augmented Generation (RAG) techniques based on Large Language Models (LLMs). We confront the difficulties of translating nuanced TCM wisdom into actionable advice compatible with AI systems, ensuring high accuracy and relevance in personalized recommendations, and maintaining scientific rigor while preserving traditional knowledge. To overcome these obstacles, we design a unified set of prompt engineering instructions tailored for TCM dietary guidance and evaluate several mainstream LLMs, ultimately selecting Qwen as the optimal base model. By integrating RAG with a specialized TCM knowledge base, we enhance the model’s accuracy and professionalism; experimental results show significant improvements, with the ROUGE-L score increasing from 0.294 to 0.427 and the Accuracy score rising from 0.315 to 0.439. Case studies further demonstrate that our method enhances the rationality and customization of recommendations, ensuring they are scientifically sound and tailored to individual needs. This approach significantly improves the relevance and fidelity of TCM-based dietary recommendations, bridging traditional wisdom and modern technology for personalized healthcare.