In recent years, smart cities and their enabling technologies, such as the Internet of Things (IoT), have grown and expanded significantly. This trend has led to an exponential increase in sensors and smart devices and, as a result, facing the phenomenon of streaming big data continuously being produced with high volume, speed, and variety. This issue has created an unprecedented opportunity for businesses and companies to exploit this data through a new generation of data-driven applications. However, the real-time, dynamic, non-stationary nature and the changing statistical patterns of big data streams in the IoT present data analysis with a challenge called concept drift as the leading cause of the gradual decline in efficiency and unreliability of static machine learning models. One of the research challenges of this field is how to deal with this issue and make real-time decisions in dynamic and complex situations. Although concept drift and IoT have each been independently researched in the literature, the confluence of these subjects has not yet received any attention. Consequently, this paper serves as a beginning step in addressing the problem in this area. After discussing the significance and various notions of the concept drift, this paper will highlight some of the latest research works in this field. To conclude, the paper will present this field\\\'s challenges and research opportunities.

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