In the era of artificial intelligence (AI), city branding is no longer viewed as the outcome of marketing and communication activities alone but as a dynamic construct emerging from the interaction of official, citizen-generated, spatial, and digital data. Despite the rapid growth of urban data and AI applications, an integrated governance framework for transforming fragmented data into actionable knowledge for urban policymaking remains underdeveloped. This study develops a data-driven urban brand governance model using a grounded theory approach. Adopting an applied qualitative design, data were collected through semi-structured interviews with experts in urban management, city branding, data governance, artificial intelligence, and public policy until theoretical saturation was achieved. Following Strauss and Corbin's systematic approach, data were analyzed through open, axial, and selective coding. The analysis generated 406 initial codes, refined into 72 concepts, 12 subcategories, and 6 main categories. Findings reveal that data-driven urban brand governance is shaped by increasing urban complexity, growing data diversity, AI-enabled analytical capabilities, and the shift from promotion-oriented branding toward lived experiences. The proposed model comprises three strategic dimensions: urban brand data integration, AI-enabled brand intelligence and analysis, and intelligence-driven decision-making. The process model conceptualizes governance as a continuous learning cycle linking diverse data sources, intelligent analysis, dynamic urban brand intelligence, policy decisions, policy feedback, and new data generation. The study integrates city branding, data governance, and artificial intelligence into a unified governance framework, providing a foundation for adaptive, evidence-informed urban policymaking in the AI era.