Volume 2 - Issue 3, May - June 2026

Natural Language Processing (NLP) has significantly improved the development of intelligent conversational systems for database interaction. Traditional database management systems generally require structured query languages such as SQL, which may not be convenient for non-technical users. This paper presents an Automated Natural Language Database Query System using NLP and Dialogflow for simplified conversational database access. The proposed system enables users to perform database operations through natural language interaction using a chatbot interface. The system integrates Dialogflow for intent recognition, FastAPI for backend processing, Python for application logic, and MySQL for database management. User queries are analyzed using NLP techniques, converted into corresponding database operations, and processed automatically through webhook communication. A food ordering application is implemented to demonstrate functionalities such as order creation, item management, order tracking, and information retrieval through conversational interaction. The developed system improves accessibility, reduces dependency on structured query knowledge, and provides an efficient human– computer interaction model for database-driven applications. The proposed approach can be extended to domains such as e-commerce, customer support, and enterprise information systems.

Lambade Venkatesh, Dr.D.William Albert, G S Udaya Kiran Babu,"Automated Natural Language Database Query System Using NLP and Dialogflow" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(484-491) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.