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Now a days, air pollution is a critical environmental issue in developing countries due to urbanization and industrialization. The pollution in air causes severe health issues for human beings, affects plants and animals due to acid rain, creates loss in economy, agricultural production and commercial production. In order to prevent or control it, the air quality forecasting necessitates. Accurate air quality prediction is a prominent task to be done to control the pollution level and to take countermeasures for securing the human life. The complex, uncertain and non-linear nature of the air quality pollutants impose complications in achieving accurate forecasting. The advent of artificial intelligence techniques helps to improve the air pollution forecasting results compared to traditional statistical methods at the great extent. This paper focuses the complications of air pollution, significance of air quality prediction, air quality index, and reviews the applications of machine learning and deep learning methods in air quality prediction. Literature shows that the machine learning and deep learning methods along with feature engineering and optimization techniques guarantees a promising air quality prediction. [ABSTRACT FROM AUTHOR]