Tourism experiences are shaped within digitally and algorithmically structured environments. Artificial intelligence (AI) actively mediates tourist interactions and decision-making processes. This study draws upon a critical narrative review of research on tourist decision-making, satisfaction, and AI. It conceptualizes AI-driven tourism personalization as a cognitive-emotional appraisal mechanism through which tourists interpret and assess algorithmically generated experiences. The proposed framework integrates AI-enhanced travel stimuli, personal and social determinants, with AI-driven personalization (as a cognitive-affective mechanism), smart tourism technologies (STTs), algorithmically pre-structured expectations, contextual conditions, satisfaction, and behavioral outcomes across the pre-trip, on-site, and post-trip stages. This approach clarifies that satisfaction stems from how tourists interpret algorithmic stimuli in relation to their own expectations, emotions, cultural values, and perceived autonomy. The framework provides both a theoretical foundation for empirical research and practical guidance for destination managers seeking to design AI-supported personalization strategies that leverage algorithmic efficiency to enhance traveler satisfaction.