P.Krishna Murthy, I.Haritha 03 06 The growing demand for personalized travel experiences has driven the need for intelligent itinerary planning systems capable of adapting to dynamic user preferences and realtime information Traditional travel recommendation systems often rely on static datasets and lack contextual awareness resulting in generic and sometimes irrelevant itineraries This paper proposes a Travel Plan Itinerary Generator using RetrievalAugmented Generation RAG a hybrid AI approach that integrates external knowledge retrieval with large language models LLMs to generate accurate contextaware and personalized travel plans The proposed system leverages vector databases semantic search and transformerbased models to retrieve relevant travel information such as destinations accommodations weather forecasts and local attractions This retrieved data is then used to augment the generative process ensuring factual correctness and reducing hallucinations Experimental evaluations demonstrate that the RAGbased approach significantly improves itinerary relevance user satisfaction and adaptability compared to conventional systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026