University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 2 2026 06 01 A Strategic Framework for Smart Oversight Based on Machine Learning in Urban Management in Iran: A Grounded Theory Approach (Case Study: Tehran Municipality) A Strategic Framework for Smart Oversight Based on Machine Learning in Urban Management in Iran: A Grounded Theory Approach (Case Study: Tehran Municipality) 263 281 240721 10.22034/judpm.2026.562470.1076 FA Bahare Pouryamini PhD Student in Urban Planning, Faculty of Social Sciences, Allameh Tabatabai University, Tehran, Iran Alireza Jalinous PhD Student in Information and Communication Technology Management, Faculty of Administrative and Economic Sciences, University of Ferdowsi mashhad, Iran ,Ferdowsi University, Mashhad, Iran Journal Article 2025 10 23 <span>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 using a grounded theory approach. Data were collected through semi-structured interviews with 12 experts in urban management, administrative supervision, information technology, and data governance and analyzed through open, axial, and selective coding. The findings identified three main categories influencing intelligent monitoring: causal conditions, contextual conditions, and intervening factors. Four key strategies were also extracted, including the development of AI infrastructure, human empowerment, intelligent policymaking, and machine learning-based monitoring indicators. The results indicate that intelligent monitoring can enhance transparency, reduce corruption and administrative deviations, improve data-driven decision-making, and strengthen public trust in urban management. The proposed model provides a practical framework for implementing intelligent monitoring in municipalities.</span> <span>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 using a grounded theory approach. Data were collected through semi-structured interviews with 12 experts in urban management, administrative supervision, information technology, and data governance and analyzed through open, axial, and selective coding. The findings identified three main categories influencing intelligent monitoring: causal conditions, contextual conditions, and intervening factors. Four key strategies were also extracted, including the development of AI infrastructure, human empowerment, intelligent policymaking, and machine learning-based monitoring indicators. The results indicate that intelligent monitoring can enhance transparency, reduce corruption and administrative deviations, improve data-driven decision-making, and strengthen public trust in urban management. The proposed model provides a practical framework for implementing intelligent monitoring in municipalities.</span> https://www.judpm.ir/article_240721_8c88b34cd6ffd2d702864871eafd7091.pdf