Informality has a considerable impact on modern economies and societies, including by influencing progress toward the Sustainable Development Goals (SDGs). In this research, we developed an innovative analysis of these relationships by integrating informal indicators into SDG predictive models. To achieve this, we employed machine learning methods—such as Random Forest and XGBoost—and feature selection techniques (Chi-squared and Mutual Information), thereby introducing a novel element to studies on the informal economy. The results highlighted the complexity of the relationship between informality and sustainability, showing that CO₂ emissions, agricultural income, and workplace safety emerged as key determinants. Moreover, the inclusion of informal indicators in the analysis enhanced the accuracy of the estimations. Thus, the methodological contributions of this study provided a robust and replicable framework useful for informing future public policies aimed at sustainable development.