Air pollution has been a major issue in recent decades owing to increasing industrial, agricultural, and urban waste disposal. The fast rise in polluting material in the atmosphere affects the climatic condition, alters the atmosphere, and harms human health. The negative consequences of air pollution have prompted academics to anticipate and calculate the Air Quality Index (AQI) using factor
... [Show full abstract] extraction and regression methods. To solve this issue, this study proposes an IoT-based hybrid model that combines Factor Analysis (FA), Artificial Neural Networks (ANN), and Auto-Regressive Moving Average (ARMA) methods. The Factor Analysis (FA) model is used to extract polluting components, followed by Artificial Neural Networks (ANN) to regress the projected rate. The suggested hybrid model's quantitative analysis shows an increase in accuracy from 76.2 percent to 94.8 percent while simplifying the procedure. The suggested IoT-based hybrid model may be used to estimate the proportion of polluting components in the air.