The Amazon Rainforest, a critical hub of global biodiversity, faces increasing anthropogenic pressures, necessitating effective monitoring and conservation strategies. Species Distribution Models (SDMs) are vital tools for predicting habitat suitability, yet advanced machine learning-based SDMs, while accurate, often suffer from limited interpretability. This “black box” nature restricts ecological understanding and hinders the translation of model outputs into actionable conservation insights, a limitation evident in previous studies on Amazonian avifauna which primarily identified correlations without fully disentangling the influence of individual environmental drivers. This study overcomes these limitations by integrating Explainable Artificial Intelligence (XAI) techniques with SDMs to specifically assess the impacts of anthropogenic atmospheric pollutants on sensitive bird species from the Tyrannidae and Thraupidae families. We leverage unique, high-resolution atmospheric data (including aerosols and trace gases) from the GoAmazon 2014/15 campaign and apply SHapley Additive exPlanations (SHAP) to interpret a high-performing Support Vector Machine (SVM) model predicting bird occurrence. Our findings reveal that atmospheric pollutants strongly associated with human activity–notably methane, carbon monoxide, ozone, and acetonitrile–are among the most significant predictors of habitat suitability. SHAP analysis quantifies the complex, often non-linear contributions of these variables, demonstrating how specific pollutants drive habitat suitability predictions locally and globally. By enhancing SDM interpretability, this research provides transparent, ecologically meaningful insights into environmental stressors, offering a more robust tool to support evidence-based conservation planning for Amazonian biodiversity.