AI Trip Planner using Google Gemini API

Abstract: Travel planning often involves extensive manual effort, requiring users to search, compare, and organize multiple aspects such as destinations, activities, and schedules. This process can be time-consuming and may not always result in optimal or personalized itineraries. To address these challenges, this paper presents an AI-based Trip Planner that utilizes the Google Gemini API to generate intelligent and customized travel plans. The proposed system is designed to provide personalized itinerary recommendations based on user inputs, including destination, travel duration, mood, and group type (such as solo, friends, or family). The system is developed using Python and Django for backend processing, while the frontend is implemented using HTML, CSS, JavaScript, jQuery, and Bootstrap to ensure a responsive and user-friendly interface. An SQLite database is used to store user preferences and trip history securely. The methodology involves processing user inputs, generating structured prompts, and leveraging the Gemini API to produce context-aware travel recommendations. Experimental results indicate that the system provides accurate, efficient, and user-centric itineraries with reduced planning time. This research demonstrates the effectiveness of AI-driven solutions in enhancing travel planning by improving personalization, efficiency, and user experience, thereby contributing to the advancement of intelligent tourism systems. Keywords: Artificial Intelligence, Travel Recommendation System, Google Gemini API, Smart Itinerary Planning, Personalized Travel, Django Web Application