<p>Smog is an air pollutant which effects visibility and can cause severe respiratory and ocular diseases. The air quality in smog effected areas gets worsen in case there is no natural rainfall for longer period of time. To address such critical environmental challenge, artificial rainfall techniques are employed. This study conducts a systematic literature review of research articles on IoT-based artificial rainfall systems for smog control. The review synthesizes findings on the use of sensors for pollutant detection, the gases involved in smog formation, IoT for data capturing, and machine learning for predictive analysis. Results reveal that while many studies utilize sensors to monitor pollutants, they generally do not tend to measure the effect of all relevant gases considering the major contributors of smog. Although the artificial intelligence-based techniques such as machine learning are used in some studies yet there is no clear consensus on which of them are more effective. Building on the findings, this work proposes a taxonomy depicting the prominent components of automated smog control system. In addition, this study suggests an architectural model enforcing the integration of advanced sensors and precipitation enhancement approaches. It emphasizes the use of IoT-based sensors to autonomously identify pollutants such as sulfur dioxide, particulate matter, carbon dioxide, hydrocarbons, and nitrogen dioxide, with precise calculations. Lastly, this research suggests real-time air quality data display through a dynamic web application posing intelligent precipitation enhancement assessments and the automatic alerts to signal when artificial rainfall might be necessary based on atmospheric conditions.</p>