Rapid urbanization and the increasing complexity of underground infrastructure systems in megacities such as Tehran have intensified geotechnical and structural risks, necessitating a transition from reactive monitoring toward predictive and intelligent risk governance. Although recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twin technologies have transformed smart infrastructure management, significant gaps remain regarding the integration of these technologies within developing urban governance systems. This study proposes a conceptual AI-IoT framework for predictive risk management in smart city subsurface infrastructure using a documentary and descriptive-analytical research approach. The proposed framework consists of three interrelated layers: 1) an IoT-enabled sensing layer for continuous geotechnical and structural monitoring, 2) an intelligent analytics layer employing AI-driven predictive models and Digital Twin simulations for anomaly detection and risk forecasting, and 3) a decision-support layer facilitating adaptive governance and predictive maintenance. Focusing on Tehran as a representative developing megacity, the study analytically examines institutional fragmentation, interoperability limitations, and governance barriers affecting intelligent infrastructure implementation. The findings suggest that integrated AI-IoT governance frameworks can significantly enhance urban infrastructure resilience, predictive decision-making, and adaptive risk management in subsurface smart city systems. The study contributes theoretically to cyber-physical infrastructure governance literature and provides a conceptual foundation for future empirical implementation of predictive smart infrastructure systems in developing urban environments.