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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62564
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
Matoshri Collage of Engineering and Research Centre
Computer Engineering
Matoshri Collage of Engineering and Research Centre
Computer Engineering
Matoshri Collage of Engineering and Research Centre
Computer Engineering
Matoshri Collage of Engineering and Research Centre
Computer Engineering
Matoshri Collage of Engineering and Research Centre
Tourism plays a vital role in the global economy by fostering cultural exchange, employment, and economic growth. However, trip planning remains a tedious and fragmented process, as travelers often struggle to gather reliable information, estimate costs, and adapt to real-time conditions. The AI-Powered Smart Tourism System aims to address these challenges by leveraging Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and data analytics to create a unified, intelligent platform for travel planning and assistance. The system automatically generates personalized travel itineraries based on user preferences such as interests, duration, and budget. It provides real-time updates on weather, crowd levels, and expenses while offering multilingual support and chatbot-based interaction for seamless communication. Through the integration of APIs such as Google Maps, OpenWeather, and Google Translate, along with AI frameworks like TensorFlow, Scikit-learn, and OpenAI APIs, the platform delivers dynamic and accurate recommendations. By combining intelligent automation with usercentric design, the proposed system transforms traditional trip planning into a data-driven, adaptive, and interactive experience. It promotes sustainable tourism, enhances traveler safety with SOS features and offline support, and encourages the exploration of lesserknown destinations. The project demonstrates how AI can revolutionize modern tourism, offering smarter, safer, and more personalized travel experiences for users worldwide.
KEYWORDS
The AI-Powered Smart Tourism System represents a major leap forward in how travelers interact with technology to plan, manage, and enjoy their journeys. By integrating artificial intelligence and data analytics, the system converts conventional travel planning into an intelligent, adaptive, and user-centered process. It effectively overcomes the limitations of traditional tourism platforms, such as fragmented and unreliable information, lack of personalization, and difficulties in communication due to language barriers. Through AI-driven recommendations, tourists can receive tailored suggestions for destinations, accommodations, and activities based on their preferences, budget, and current location. Moreover, the system’s ability to analyze large datasets allows it to generate accurate cost estimations, ensuring travelers can plan efficiently without unexpected expenses. Real-time assistance features—such as chatbots, language translation, and location tracking—enhance the safety and comfort of users, providing instant support during emergencies or while navigating unfamiliar environments. The use of predictive analytics further enables the system to anticipate user needs, optimize itineraries, and suggest ideal travel times or routes.
[1] R. Roriz, J. Cabral, and T. Gomes, “Automotive LiDAR Technology: A Survey,” IEEE Transactions on Intelligent Transportation Systems, 2021. [2] L. Yao, X. Liu, and Z. Zhang, “Baidu Index-Based Forecast of Daily Tourist Arrivals,” Alexandria Engineering Journal, vol. 60, no. 3, pp. 3105–3114, 2021. [3] M. Ahmad, S. Khan, and R. Ali, “Chatbots in Tourism and Hospitality: Applications and Challenges,” IGI Global, 2024. [4] T. Emara, A. Hassan, and M. Elhoseny, “Distributed Data Management for Large-Scale Analysis,” Scientific Reports, vol. 13, 2023.
Publication Date
5/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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