The health of Ayurveda is gaining popularity and is not always easily accessible due to the complexities and technical terminologies of the texts. This study describes an NLP-based Ayurvedic health recommendation system that uses named entity recognition (NER) to recognize Ayurvedic concepts such as doshas, symptoms, and therapies. A customized WordNet improves semantic comprehension, and a Retrieval-Augmented Generation (RAG) model allows for correct and contextually relevant answers to Ayurvedic literature. The system was examined using important performance measures and received a faithfulness of 0.8739 and a context recall of 0.8158, suggesting its ability to generate precise and meaningful recommendations. The custom NER model, trained particularly for Ayurvedic terminology, outperforming the standard NER models indicating improved entity recognition. This work makes Ayurvedic healthcare concepts more accessible to users by bridging the gap between traditional Ayurvedic knowledge and modern AI-driven accessibility.