Advances in artificial intelligence (AI) have transformed pricing mechanisms across the tourism and medical tourism sectors, enabling unprecedented levels of price transparency, cost forecasting, and information accessibility. This review synthesizes the existing literature on AI-based price transparency systems and examines their impact on tourist confidence and destination choice. Findings indicate that AI-driven pricing tools—such as machine learning–supported cost estimators, automated price comparison engines, and real-time dynamic pricing platforms—significantly reduce perceived financial risk by clarifying expected costs and minimizing unexpected expenses. Increased transparency enhances trust in both service providers and destinations, thereby fostering greater consumer confidence and influencing destination selection. Furthermore, AI-enabled systems support equitable competition among destinations by standardizing pricing information, while also shaping tourists’ cognitive evaluations through eWOM analytics, sentiment analysis, and predictive modelling. However, challenges remain regarding algorithmic bias, data privacy, overstandardization of prices, and potential consumer reliance on automated systems. Overall, the review demonstrates that AIbased price transparency is a critical determinant of modern tourist behavior, strengthening trust, improving decision-making, and reshaping destination competitiveness. Future research should investigate cross-cultural differences in trust formation, the long-term behavioral impacts of AI-mediated pricing, and the ethical implications of algorithmic transparency in tourism markets. This review study explores how AI-based price transparency practices in the tourism sector influence tourist trust and, in turn, destination choice. It focuses on the effects of transparent pricing, dynamic pricing, and personalized AI-driven systems on tourists’ perceptions of fairness and trust, synthesizing existing research to clarify the relationship between price transparency, trust, and destination selection.