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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62564

AI-Powered Smart Tourism System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62564

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

GN

Ghanshyam Nikam

Computer Engineering

Matoshri Collage of Engineering and Research Centre

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PS

Prajwal Shinde

Computer Engineering

Matoshri Collage of Engineering and Research Centre

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PSAH

Prof. S. A. Handore

Computer Engineering

Matoshri Collage of Engineering and Research Centre

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SM

Sahil Malunjkar

Computer Engineering

Matoshri Collage of Engineering and Research Centre

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PN

Priya Navale

Computer Engineering

Matoshri Collage of Engineering and Research Centre

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Abstract

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

Key Words: Discover Maharashtra Visual Gallery Virtual Travel Assistant Heritage & Culture Explorer Seamless Trip Planning

Cite This Article

Ghanshyam Nikam, Prajwal Shinde, Prof. S. A. Handore, Sahil Malunjkar, Priya Navale, "AI-Powered Smart Tourism System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

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.

References

[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.

Editorial & Review Transparency

Publication Date

5/5/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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4

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7

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5

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The authors declare no conflict of interest related to this research work.

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