Abstract: Wander Wish is an intelligent travel planning and destination discovery platform designed to simplify and personalize the travel experience for users. The project aims to bridge the gap between travelers and the overwhelming amount of travel information available across multiple online sources by providing a unified, user-centric solution. Wander Wish leverages modern web technologies and intelligent data processing to assist users in discovering destinations, planning itineraries, searching accommodations, and making informed travel decisions based on preferences, budget, and time constraints.The system integrates real-time data from travel service providers such as hotel booking platforms, location databases, and travel information sources to deliver accurate and up-to-date results. By utilizing intelligent recommendation logic and Large Language Model (LLM) capabilities, Wander Wish analyzes user inputs such as travel dates, interests, group size, and budget to generate personalized travel suggestions, optimized itineraries, and accommodation recommendations. This approach reduces manual effort and enhances decision-making efficiency for travelers.Wander Wish follows a modular client-server architecture, ensuring scalability, maintainability, and seamless integration with external APIs. The backend handles data aggregation, search logic, and AI-driven recommendations, while the frontend offers an intuitive and responsive user interface for effortless interaction. Security and performance considerations are incorporated to ensure safe handling of user data and fast response times.Overall, the Wander Wish project demonstrates how intelligent automation and AI-assisted decision support can transform traditional travel planning into a smarter, more personalized experience. The platform not only saves time and effort for users but also enhances travel satisfaction by aligning recommendations with individual preferences, making Wander Wish a practical and innovative solution in the modern travel technology ecosystem.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14105