Urban construction projects often face challenges in controlling time, cost, and quality due to complexity, limited resources, and the need for accurate decision making. Artificial intelligence (AI) technologies can improve construction project management and enhance efficiency in urban construction management. Therefore, this study aims to identify factors influencing the adoption of AI technologies in urban construction management and develop a model to improve municipal construction project performance. This research used a mixed method (qualitative–quantitative) approach. In the qualitative phase, 33 initial factors were identified through literature review and evaluated using a two round fuzzy Delphi method with 14 experts, resulting in 29 confirmed factors. In the quantitative phase, questionnaire data from 139 valid responses were analyzed from 218 participants. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) were applied. Results show that resource management significantly influences AI integration in construction management (0.621) and project management performance and urban innovation (0.684). AI integration strongly affects predictive project analytics (0.885), intelligent project monitoring (0.838), and project management performance (0.797). Predictive analytics also influences intelligent monitoring (0.823) and project management performance (0.649). Finally, project management performance directly affects intelligent monitoring (0.718). Overall, the findings indicate that resource management, AI integration, and predictive analytics improve performance and support intelligent monitoring in urban construction projects.