Urban noise pollution, exacerbated by traffic, construction, and high population density, seriously threatens public health and quality of life. While effective on a small scale, traditional noise monitoring methods fail to provide the real-time data needed for widespread urban applications. This paper explores the potential of the Internet of Things (IoT) technology for noise monitoring in urban environments, examining an IoT architecture that integrates Wireless Sensor Networks (WSNs), cloud computing, and machine learning to provide comprehensive noise data collection and analysis. Key case studies from Barcelona, New York, and Delhi highlight real-world applications, and challenges such as data privacy, sensor calibration, and scalability are examined with proposed solutions. By adopting IoT-enabled noise monitoring, cities can implement data-driven noise management policies that improve public health and urban living conditions.