Abstract Smart cities, leveraging advanced technologies such as the Internet of Things (IoT), Digital Twin, and Artificial Intelligence, have fundamentally transformed the management of urban infrastructure and services. These technologies enable improvements in quality of life, resource consumption optimization, and enhanced environmental sustainability. However, the complexity and diversity of factors influencing smart infrastructure development necessitate multi-criteria and uncertainty-based decision-making frameworks to select optimal solutions considering technical, operational, and economic criteria. This study employs a combination of the fuzzy Delphi method for extracting and consolidating expert opinions and the GRA-VIKOR method for multi-criteria analysis and prioritization of smart infrastructure development options. The fuzzy Delphi process models the ambiguity and uncertainty in expert opinions using fuzzy numbers, while the Grey Relational Analysis (GRA) assists in determining the relative weights of criteria. Subsequently, the VIKOR algorithm evaluates and ranks the best balanced options considering conflicts and trade-offs among criteria. The results indicate that resource and energy optimization, data integration and real-time monitoring, citizen-centric services, and sustainability are the most critical criteria in smart infrastructure development decision-making. The integrated fuzzy Delphi and GRA-VIKOR approach effectively reduces decision-making complexity and highlights optimal alternatives by balancing economic, environmental, and operational objectives. Ultimately, this method can assist urban policymakers in prioritizing smart city projects. The use of a fuzzy Delphi framework combined with GRA-VIKOR multi-criteria analysis represents an effective and scientific approach to optimizing decision-making processes in smart urban infrastructure development. By providing a structured tool for aggregating expert insights and analyzing complex criteria, this approach facilitates more precise policymaking, enhances sustainability, and improves resource efficiency in smart cities. Future research is recommended to focus on improving dynamic models and data security within this framework. Keywords: Smart cities, Artificial Intelligence (AI), Digital Twins, Decision-making, Connected Communities, Internet of Things (IoT), Real-time Monitoring