Harnessing AI, Deep Learning, and Machine Learning for Sustainable Tourism Development: Innovations in Management and Economics

Abstract

This study focuses on the changes brought out by Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) in the management of tourism within the two dimensions of economic and environmental sustainability. The research identifies from a meta-analysis of peer-reviewed studies and industry reports how AI-driven computational models optimize demand forecasting, anomaly detection, and resource allocation. Our findings show that AI adoption in tourism businesses has grown considerably, resulting in increased operational efficiency, cost savings, and revenue generation. The EfficientNet-DiCENet fusion model for anomaly detection in IoT-based tourism networks is introduced in the study with an accuracy of 96.7% and outperforms conventional AI models. Economic analysis shows 20-25% savings in operational costs with a revenue increase of up to 23%. Moreover, AI applications promote environmental sustainability through energy optimization, waste management, and reduced carbon footprint. Although advances have been made in this area, the study notes such limitations as real-world validation and recommends future research on hybrid AI models made up of blockchain and reinforcement learning. However, in conclusion, AI holds promise to lead sustainable tourism into the next phase, delivering data for informed decision-making, the economic resilience needed to withstand the impacts of climate change, and its ecological balance.