The rising importance of sustainability has elevated environmental disclosures particularly greenhouse gas (GHG) emissions as key indicators of corporate financial health. This review evaluates how machine learning (ML) methods, guided by Stakeholder and Signaling Theory, are applied to predict financial performance based on sustainability data. By systematically analyzing literature from 2020 to 2025, this study assesses the strengths, limitations, and practical applications of ML models such as decision trees, support vector machines, and neural networks. It highlights key trends, identifies methodological gaps especially in emerging markets like Indonesia and proposes directions for future research. Findings suggest that integrating ML with environmental disclosures enhances prediction accuracy and supports more informed financial decision-making.

Link: Review of Machine Learning Applications in Sustainability and Financial Prediction