A Comprehensive Overview of Transformative Potential of Machine Learning and Wireless Sensor Networks in Sustainable Urban Development

Wireless sensor networks (WSNs) have become essential elements in the advancement of smart cities, enabling the collection and analysis of data in real time for a wide range of urban applications. This article provides an exhaustive examination of current developments in the application of Machine Learning (ML) techniques to WSNs for smart cities. The paper examines a wide range of applications, such as infrastructure management, urban mobility, environmental monitoring, and reinforcement and deep learning algorithms. It emphasizes the significance of each of these types of algorithms. Significant obstacles like data security, scalability, and energy efficiency are examined, in addition to possible remedies and recommendations for future studies. By means of a methodical examination of extant scholarly works, this review provides scholars, practitioners, and policymakers with significant perspectives that promote sustainable growth and innovation within smart city ecosystems.