The fast expansion of Internet of Things (IoT) devices in urban environments has resulted in a dramatic increase in both the volume and complexity of data produced, necessitating the implementation of sophisticated data analytics and machine learning methodologies to fully realize the advantages of smart cities. The incorporation of IoT sensors and devices has facilitated the establishment of extensive, dynamic, and diverse networks, which present considerable challenges for data analysis and decision-making processes. In response to these challenges, machine learning algorithms have surfaced as a feasible solution, capable of discerning intricate patterns and relationships within the data
