The growing complexity of urban ecosystems, fueled by rapid population expansion, is intensifying the challenges cities face in achieving sustainability, circularity, and resilience. This evolving landscape demands innovative management approaches that integrate technological advancements and resource optimization strategies to support resilient urban development. This research proposes a management-oriented framework for Artificial Intelligence enabled Industrial Symbiosis networks within smart cities, focusing on enhancing resource recovery and establishing closed-loop systems. By leveraging AI technologies of predictive analytics, dynamic matchmaking, and optimization algorithms, the framework aims to identify, coordinate, and scale symbiotic partnerships among industries, utilities, and municipal stakeholders. Rather than emphasizing the technical development of AI algorithms, the study addresses strategic governance models, stakeholder engagement mechanisms, and policy interventions that facilitate the effective integration of AI in managing industrial symbiosis networks. Drawing on principles of the circular economy and smart infrastructure governance, this research highlights how AI can drive adaptive and resilient resource flows within urban environments. By bridging technological innovation with strategic management practices, the study contributes to the evolving discourse on sustainable smart city development and offers actionable insights for policymakers, city planners, and infrastructure managers.