Document Type : Review
Authors
1
Associate Professor, Department of Geography, Tourism Research Center, Urban Planning, Najafabad branch, Islamic Azad University, Najafabad, Iran
2
Assistant Professor of Tourism Research Center, Najafabad Branch Islamic Azad University Najafabad Branch Iran.
10.22034/gp.2026.66784.3391
Abstract
This research, conducted in 2025, is a qualitative, applied study based on library and documentary research. It was carried out using a descriptive-survey method with a scientometric approach, incorporating citation analysis, systematic reviews, bibliometric analysis, and lexical co-occurrence analysis of studies related to artificial intelligence and urban development. The study utilized VOSViewer software to identify the most influential vocabulary in the research topic. A systematic review of articles revealed nine thematic clusters that directly influenced the role of artificial intelligence in urban development. The most frequently examined keywords in these clusters, ranked by importance, included "artificial intelligence," "machine learning," and others. The study identified three main clusters: 1) Sustainable energy management (red), 2) Smart traffic and transportation (green), and 3) Sustainable urban planning (navy blue). Additionally, researchers such as Yigitcanlar and journals like Sustainable Cities and Society were prominent in the reviewed studies. The findings indicated that artificial intelligence in urban development emphasizes data-driven solutions, but issues such as sustainability, ethics, and privacy have received less attention. Furthermore, the limited number of studies on developing countries suggests that the application of artificial intelligence in urban development in these regions is overlooked compared to leading countries like China and the United States. The results demonstrated that artificial intelligence, with its significant performance benefits, is one of the best options for urban management to support sustainable urban development. AI can optimize infrastructure, reduce energy consumption, and enhance urban sustainability. However, the scattered nature of research and the lack of comprehensive analysis in this field highlight the need for scientometric studies
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