Classified as the most hazardous air pollutant globally, PM2.5 draws significant interest from scholars and the public, especially regarding human health. Leveraging machine learning techniques, this study investigates the determinants of these emissions in multi-social dimensions. The conclusions denote that the complexity and implications of airborne particulate pollution are shaped by multifaceted social variables, economically and politically. Machine learning methods unravel multi-dimensional societal causes in PM2.5 emissions within the context of health expenditure and economic contributions from urban population growth, agriculture, forestry, and fishing, value added (% of GDP), manufactures exports (% of merchandise exports), current health expenditure (% of GDP), asserting the significance of an interdisciplinary approach in combating this global issue.