This study explores the relationship between environmental, social, and governance (ESG) factors and systemic risk, measured through conditional beta, in a sample of Eurostoxx 600 firms from 2015 to 2021. Using machine learning models, including Random Forest and XGBoost, we examine how ESG dimensions interact with the financial performance across six major super-sectors. Our findings reveal significant sectoral heterogeneity. Environmental investments increase the short-term risk in Industrial and Consumer Discretionary sectors due to the compliance costs and market volatility, while governance plays a key role in Energy and Utilities, where regulatory requirements can heighten the systematic risk. In contrast, ESG factors have a lower impact in the Financial and Real Estate sectors compared to that in the other super-sectors, where existing regulations may mitigate additional ESG-driven risk, though governance remains the most influential pillar. In the Healthcare sector, environmental initiatives appear to reduce risk by strengthening reputational capital and investor confidence, while social and governance factors increase short-term volatility. These insights suggest that ESG does not function as a universal risk mitigator but reshapes the risk exposure depending on the industry dynamics and regulatory constraints. Our results provide guidance for investors and policymakers in integrating ESG considerations into financial risk models and highlight the need for sector-specific ESG strategies to enhance the accuracy of risk assessments.