Wind energy in urban areas is a prominent alternative source of renewable energy that is becoming increasingly popular. The technological development, low cost of installation, and availability of wind make it a promising and essential renewable energy sector. Wind farms in urban settings, on the other hand, have yet to be commercialized in a meaningful way. In metropolitan areas, the urban wind becomes turbulent and unpredictable to some extent due to high-rise buildings and variations in their structures, railway tracks, and roads. Hence traditional wind mapping technologies have proven ineffective in metropolitan areas, preventing its commercial use. This paper presents a thorough analysis of the most recent urban wind-resource evaluation techniques, covering multiple strategies, methodologies, software instruments, and the impact of site and building on the accurate installation of wind farms. Noise and vibrations, as well as aesthetic and shadow effects, ecological impact, high investment prices, and data availability, have all been considered, with potential solutions proposed. It has been found that in metropolitan settings, the traditional method of analyzing wind resources is hampered by the inability to collect data in a turbulent environment. As a result, this study predicts that artificial intelligence, such as machine learning, deep learning, and multiple neural networks, will dominate this industry in order to provide precise forecasts while minimizing the operational time and costs of the utilization system.