Citation: Eco-Environment & Health (2024). DOI: 10.1016/j.eehl.2024.01.006 The rapid growth of environmental data presents a significant challenge in analyzing complex pollution networks . While ML has been a pivotal tool, its widespread adoption has been hindered by a steep learning curve . This research introduces a user-friendly framework designed to democratize the application of machine learning in environmental studies .