Tourism concentration at popular destinations creates economic imbalances, with limited spillover benefits reaching nearby regions. The study addresses this challenge by developing artificial intelligence (AI)-driven personalized nudge strategies to redistribute tourist flows, using Naoshima Island and Tamano City, Japan, as case studies. The study employed self-organizing maps (SOMs) combined with k-means clustering to analyze the behavioral patterns of 55 international tourists during summer 2023. The analysis identified three distinct segments: young art-seeking tourists (33%), characterized by high social media engagement and spontaneous decision-making; middle-aged convenience-oriented tourists (36%), who prioritize structured experiences and are highly sensitivity to language barriers; and older recreation-focused tourists (31%), who demonstrate a strong interest in cultural authenticity and the highest spending levels. Based on these behavioral profiles, the study proposes cluster-specific nudge interventions: social proof and gamification for young tourists, default options and simplified information for convenience-oriented visitors, and value-added cultural experiences for recreation-focused travelers. The framework integrates machine learning analytics with behavioral economics to provide tourism authorities with actionable, evidence-based strategies projected to increase multi-destination visits by 25%, while respecting tourist autonomy, promoting sustainable destination management, and ensuring equitable economic distribution.