The intersection of behavioral economics (BE) and artificial intelligence (AI) has grown rapidly, yet its intellectual structure remains unmapped. This study aims to provide a bibliometric mapping of the BE-AI research field, identifying key themes, influential works, and research gaps. Using bibliometric analysis with the bibliometrix R-package (version 4.1) on 252 documents from Scopus (2006–2025), we performed performance analysis, co-citation, co-word, and collaboration network analysis. Annual output grows at 16.76%, with a surge since 2018. Loewenstein and Reich are the most influential authors; Behavioral Public Policy leads journals; UK, US, Sweden, and Germany dominate citations. Co-citation reveals four clusters: (1) behavioral research & technology acceptance, (2) machine learning, (3) financial decision-making (prospect theory), (4) AI & consumer behavior. Thematic mapping shows decision-making and public policy as motor themes; environmental economics is emerging. The field lacks interdisciplinary collaboration and macro-level studies. Future research should focus on AI-based field experiments and behavioral macroeconomics. Policymakers and technology designers can use these clusters to prioritize investments in AI nudge systems and behavioral public policy.