Every day, people breathe in about twenty thousand times, which would amount to approximately thirteen to fifteen thousand liters of air. Therefore, it is inevitable to conclude that the main component of life on Earth is air. As the main component, it is necessary for the survival of all living beings. Air is a mixture of gases, primarily nitrogen and oxygen, followed by carbon dioxide, argon, neon, hydrogen, and others. Considering the amount of air that people breathe in daily, it is important to take care of the quality of air that is inhaled. Any air pollution affects human health, proper body work and activity, blood circulation, proper brain work, and therefore the quality of life. There are a large number of parameters that influence air pollution. However, most of the particles and gases that affect air quality are human-made and mainly originate from urban and industrial areas. For example, large cities such as Beijing or Delhi have a problem with dark smog due to high population and industrialization. The concentration of pollutants in the air together with the associated health risks can be represented by the air quality index, AQI. The air quality index can be determined "manually" by measuring the influence of individual parameters; temperature, pressure, visibility, and similar but also summarizing their effects. But, to determine the influence of individual parameters on AQI more quickly and simply, the application of the machine learning algorithms combined with one of the available programming languages is possible. Therefore, the goal of this work is to find a model that best predicts the air quality index by applying machine learning algorithms and the R programming language and to compare the measured values of the air quality index with the values obtained by prediction.