In recent years, the increasing complexity of administrative processes and the expansion of organizational data have highlighted the need for intelligent and data-driven monitoring in urban management. This study aimed to design a contextual model of intelligent monitoring for Tehran Municipality. Adopting a qualitative approach based on grounded theory, the research identified the conditions, strategies, and outcomes of implementing intelligent monitoring. Data were collected through semi-structured interviews with 16 participants, including city managers, monitoring and administrative health experts, IT specialists, and data governance professionals, and analyzed using open, axial, and selective coding.The findings indicate that the implementation of intelligent monitoring is influenced by three categories of factors: causal conditions (technical deficiencies, institutional and procedural motivations), contextual conditions (data infrastructure, digital maturity, and overarching smart governance policies), and intervening factors (organizational resistance, technical limitations, algorithmic bias, and privacy challenges). Based on these findings, four key strategies were identified: developing data and AI infrastructure, human and cultural empowerment, smart regulation and policymaking, and designing machine learning-based indicators and alerts.The results suggest that, when technical, institutional, and cultural prerequisites are simultaneously met, intelligent monitoring can enhance transparency, reduce deviations and corruption, improve data-driven decision-making, and strengthen public trust in urban management. The proposed model can serve as a policy and implementation framework for establishing intelligent monitoring in municipalities.