The modern and motorized way of life has cultured air pollution. Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan. Hence, this inquiry was carried out to propose an architecture design that could make a real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019), including 647,512 instances and 11 attributes. The four unique algorithms were employed: Random Forest, Linear Regression, Decision Tree and XGBoosting. It was inferred that XGB is more promising and feasible in predicting the concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was also found that the Random Forest was preferred mainly in the previous studies related to air pollution prophecy. At the same time, many probes supported that air pollution is detrimental to human health, exceptionally long-lasting exposure that causes lung cancer, respiratory and cardiovascular diseases.