## 1 Introduction  
The numbers show that more and more people are moving to cities. According to United Nations (UN) urban population as of 2018 is about 55.3% [1] and by 2050 it will become 68% [2]. Growth of urbanization causes some problems related to different aspects of life, such as transportation, health care, air quality, etc. The smart city concept was created to solve these problems, which by integrating Information and Communication Technology (ICT) with citizens and existing resources can support sustainable development and life quality improvement. There are different definitions describing smart cities, such as 'A smart city is a city in which there are six main components including smart economy, smart transportation, smart environment, smart citizens, smart life, and smart management' [3] or 'The use of smart computing technologies to make the critical infrastructure components and services of a city-which include city administration, education, healthcare, public safety, real estate, transportation, and utilities--more intelligent, interconnected, and efficient' [4]. Using the services built around the smart city notion allows us to capture a huge amount of data about the current situation and to see the real picture all over the city. The availability of data provided by sensors is a significant feature of smart cities [5,6]. From the above-mentioned issues and definitions, we can notice that air quality is considered to be an essential component in the smart city concept. Air quality has become a massive problem in many areas. According to World Health Organisation (WHO), every year more than seven million persons are dying because of this problem and more than 80% of urban areas population lives in places where air quality rises over WHO guideline limits [7]. As reported by [PERSON] et al. [8], the global and national life expectancy has been reduced because of air pollution. The study shows that in 2016 particulate matter with a diameter equal to 2.5 micrometres (PM\({}_{2.5}\)) reduced global life expectancy about 1.2-1.9 years in some polluted countries of Asia and Africa. According to the following research [9] PM\({}_{2.5}\) has severe effects for human life, becoming the reason of about 3% of mortality from cardiopulmonary disease, 5% of mortality from cancer of the trachea, bronchus, and lung, and about 1% of mortality from acute respiratory infections in children under five year. This study [10] presents that PM\({}_{2.5}\) in 2015 was the fifth-ranking mortality risk factor. Therefore, it is a crucial problem to prevent or reduce consequences caused by air pollution. Having information about air quality will induce us to make protective measures; it can lead the population to apply their daily activities in the places which are less polluted (by escaping high polluted areas). However, analysing the data, giving smart solutions remains as a challenging task. Thus, it is essential to apply productive methods and techniques for more effectively and more efficiently analysing big data, converting the invisible to visible, and extracting information hidden behind data. This paper aims to review the articles related to air pollution prediction in smart cities using machine learning techniques, to make a comparison of methodologies that different authors have been used and to get an overall idea about applied approaches. The usage of machine learning techniques in this area has begun to be actively developed, and many studies and observations have been done, which is conditioned by the importance of the field. The combination of all the information will help us to detect the tendency, to find out the innovations applied in the research area, which, in turn, will direct and guide us for future exploration. We selected the most relevant papers from the most popular databases by applying different filters based on several criteria, which are represented in detail in the next section.