This paper empirically analyzes the determinants of corporate greenwashing behavior to enhance forecasting and mitigation of greenwashing practices, particularly in the context of stakeholder decision-making. Using company-level characteristics from a sample of STOXX Europe 600 constituents, we show that ESG and environmental (E) scores exhibit a U-shaped relationship with greenwashing, indicating that companies with both low and high (E)SG scores are more likely to engage in greenwashing. Additionally, ESG disclosure score, company size, cash-to-assets, and capital intensity are positively associated with greenwashing behavior. Furthermore, greenwashing behavior is more prevalent in consumer-related industries than in other industries. Building on the identified determinants of greenwashing behavior, we develop machine learning models grounded in economic theory to forecast greenwashing risk. Overall, our analyses demonstrate how current and future greenwashing risks can be effectively assessed. This enables stakeholders such as investors and policymakers to better identify corporate greenwashing behavior and incorporate the associated risks into their decision-making.