AI Powered Travel Planner

In this paper, we present the design and development of an AI-powered "AI Trip Planner" application aimed at enhancing the travel planning experience for users in India. The system integrates machine learning models to provide personalized travel recommendations based on user preferences, trip budget, and health-related factors. By utilizing multiple APIs, such as Google Maps Platform and Open-Weather API, the application offers dynamic features like cost simulation, trip prediction, weather updates, and real-time itinerary adjustments. The system is structured around a user-centric design, including features like registration, login, trip initialization, cost prediction, budget feasibility assessment, adaptive re-planning, and real-time monitoring. Key functionalities include trip planning, feedback storage, and continuous system improvements based on user data. The integration of machine learning algorithms and third-party APIs ensures personalized and accurate trip recommendations, with the goal of optimizing travel experiences. The system also enhances future trip planning by learning from user feedback, providing a scalable solution for users seeking to plan trips effectively and efficiently. The paper discusses the architecture, methodology, and key components, offering a comprehensive solution for smarter travel planning.

Keywords—AI Trip Planner, Machine Learning, Personalized Travel Recommendations, Budget Feasibility, Cost Prediction, Adaptive Re-planner, Google Maps API, Open-Weather API, Dynamic Itinerary, Real-Time Monitoring, User Feedback, Travel Planning.