Air pollution is a significant environmental challenge with far-reaching consequences for public health and the well-being of communities worldwide. This study focuses on air pollution in the United States, particularly from 1990 to 2017, to explore its causes, consequences, and predictive modeling. Air pollution data were obtained from an open-source platform and analyzed using regression models. The analysis aimed to establish the relationship between "Deaths by Ambient Ozone Pollution" (AOP) and various predictor variables, including "Deaths by Household Air Pollution from Solid Fuels" (HHAP_SF), "Deaths by Ambient Particulate Matter Pollution" (APMP), and "Deaths by Air Pollution" (AP). Our findings reveal that linear regression consistently outperforms other models in terms of accuracy, exhibiting a lower Mean Absolute Error (MAE) of 0.004609593 and Root Mean Squared Error (RMSE) of 0.005541933. In contrast, the Random Forest model demonstrates slightly lower accuracy with a MAE of 0.02133121 and RMSE of 0.03016053, while the Huber Regression model falls in between with a MAE of 0.02280993 and RMSE of 0.04360869. The results underscore the importance of addressing air pollution comprehensively in the United States, emphasizing the need for continued research, policy initiatives, and public awareness campaigns to mitigate its impact on public health and the environment.

Keywords : Air pollution, Ambient Ozone Pollution, United States, health impacts, predictive modeling, linear regression, Random Forest, Huber Regression.