International Journal of Technology and Applied Science

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A Comprehensive Review on AI-Driven Tourist Inflow Prediction and Recommendation Systems

Author(s) Tejasvi Omkar, Sakshi Evane, Alok Sahu, Piyush Dhote, Shashank Mane
Country India
Abstract Global economic growth and cultural enrichment are significantly influenced by tourism. However, because of variables like events, climate, and seasonal variations, forecasting tourist inflow is still difficult. This study introduces a predictive framework powered by AI that uses time-series analysis and machine learning to estimate the number of tourists and offer tailored travel suggestions. The system trains models such as Random Forest, XGBoost, and LSTM (Long Short-Term Memory) networks using historical visitor data, weather trends, and event information. To guarantee accuracy and dependability, the models are assessed using RMSE and MAE metrics. A web interface built on Flask illustrates anticipated travel patterns and recommends the best times to visit. According to experimental findings, the suggested method greatly increases the forecasting accuracy of visitor flow and supports smart tourism.
Keywords Smart Tourism, Machine Learning, Time-Series Forecasting, LSTM, Random Forest, AI Recommendation System, Predictive Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, Array 2026
Published On 2026-01-01
Cite This A Comprehensive Review on AI-Driven Tourist Inflow Prediction and Recommendation Systems - Tejasvi Omkar, Sakshi Evane, Alok Sahu, Piyush Dhote, Shashank Mane - IJTAS Volume 17, Issue 1, Array 2026.
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