## 1 Introduction  
The industrial revolution and modernization have led us to a new era of science and technology. On the one hand, it has opened new horizons for transportation, trade, mining, agriculture, and urbanization. On the other hand, it has become a vital factor in polluting air, soil, and water. In the last two decades, many environmental researchers have been monitoring the quality of ambient air. Particulate matter (PM) is found to be the most dangerous kind of air pollution among various other air pollutants. After a study done by the World Health Organization (WHO) and the InternationalAgency of Research center (IARC), PM in ambient air has been categorized as 'carcinogenic' [1,2]. PM2.5 are the fine particulate matters with size less than 2.5 micrometer which are the major cause of allergies, pulmonary, and cardiovascular diseases, morbidity, and mortality. Various epidemiological tests [3] have shown a direct relationship between PM pollution with respiratory infections and cardiovascular diseases. WHO declares ambient air pollution, especially fine particulate matter, has the most adverse effect on human health, which is mostly emitted by industries, power plants, households, biomass burning, and vehicles [4]. WHO has also estimated that increasing levels of PM have played a major role in causing lung cancer, chronic obstructive pulmonary disease (COPD), ischemic heart disease, and stroke, thus leading to premature deaths.  
In this era of big data and Artificial Intelligence (AI), it is important to estimate the concentration of fine particles in the air so that people can take precautionary measures to prevent from alarming levels of high air pollution concentrations. Various deterministic models have been used for the prediction of PM2.5 concentration and other air pollutants. Several studies have been done to estimate the air pollutants concentration using numerous modeling techniques [5] including statistical, Machine Learning (ML), and photo-chemical models [6].  
The objectives of this paper are as follows: (1) Analyze the features that are highly correlated with the PM2.5 concentration, such as meteorological parameters (temperature, wind speed, relative humidity, surface roughness, planetary boundary layer, and precipitation) and pollutants' concentrations (PM10, CO, NO2, SO2, and O3). (2) The pollutants' concentration variables can be measured by monitoring stations at specified locations and also predicted by the CMAQ model. After combining the features predicted by CMAQ model (elemental carbon (EC), ammonium (ANH4), nitrate (ANO3), and miscellaneous pollutants (OTHR) concentration), the results of ML models have improved (3) Design and optimize six recently used state-of-the-art machine learning models and compare their average performances. We choose two recent and most widely used tree-based models, XGBoost and LightGBM, which fall under the category of machine learning; four popular Deep Learning (DL) neural networks named Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and convolutional-LSTM (CNNLSTM); and a combination of Bidirectional and Unidirectional LSTM (BiULSTM) for the prediction of PM2.5 concentration. Among these, LSTM network outperforms other well-known models.