Smart Tourism: AI-Driven Personalization in Tourism: Enhancing Customer Experience Through Machine Learning Algorithms

The rapid expansion of digital tourism and traveler data presents a significant opportunity for personalization, yet existing systems struggle to adapt to ever-changing tourist preferences within context. This study proposes a novel multi-dimensional artificial intelligence (AI) framework leveraging advanced machine learning for hyper-personalized, real-time tourism experiences. The architecture integrates transformer-based neural networks, reinforcement learning agents, and ensemble methods to process heterogeneous data streams, including user behavior, social media, environment, time, and culture. The method used was a hybrid method combining the collaborative filtering recommendation system along with the content-based recommendation system along with improvement using real-time adaptation and optimization algorithm. As more and more tourists started utilising the tech support service, the framework was validated through a large deployment at various tourism ecosystems. Also, the analysis included the recognition of 2.3 million users interactions and 450000 during service transactions in 15 nations. Results show a significant increase in recommendation accuracy for single-item recommendations (78% improvement), personalization accuracy (91.2%), and satisfaction score (65% increase) compared to conventional systems. The system based on AI take less than 0.4 s for a response while reducing computing overhead by 42% through algorithm. This study introduces an innovative approach to intelligent tourism systems that utilizes real-time contextualized information and predictive behavior models to improve tourist satisfaction. Innovative mathematical frameworks for modelling tourism preferences and new cross-cultural adaptation techniques. The practical implications are better experience for customers, revenue maximization for tourism operators and sustainable tourism development through optimal allocation of resource.