FLIGHT BOOKING ASSISTANT USING GENERATIVE AI

Abstract

The Next-Generation Flight Booking Assistant powered by Generative Artificial Intelligence (Gen AI) represents a transformative shift in how travelers search, compare, and book air travel. Traditional flight booking platforms rely heavily on rule-based systems and static filters that often fail to capture user intent, contextual preferences, and dynamic market conditions. This project proposes an intelligent, conversational, and adaptive flight booking assistant that leverages Generative Adversarial Networks (GANs) along with large language models to provide personalized, real-time, and user-centric booking experiences. The system is designed to understand natural language queries, generate optimized travel itineraries, predict fare trends, and simulate multiple booking scenarios to recommend the most costeffective and convenient options. By learning from historical booking data, seasonal demand patterns, and user behavior, the assistant continuously improves recommendation quality. The integration of GANs allows the system to generate synthetic but realistic travel data to enhance model robustness, address data sparsity, and improve prediction accuracy under uncertain or rapidly changing conditions. Overall, the proposed system aims to reduce user effort, increase booking efficiency, improve customer satisfaction, and support airlines and travel platforms with intelligent demand forecasting and decision support. This research highlights the role of Gen AI in building scalable, adaptive, and future-ready flight booking ecosystems.