This study evaluates the efficacy of ARMA-GARCH models in forecasting the volatility of Environmental, Social, and Governance (ESG) stock indexes, leveraging comprehensive data from S&P. Amidst the increasing integration of sustainable investment criteria into financial strategies, this research focuses on the applicability of these models to capture the unique volatility characteristics inherent to ESG indexes. Through rigorous data preprocessing and model validation, the research confirms that ARMA-GARCH models are effectively used to predict the volatility of ESG indexes. This study not only provides a methodological framework for integrating ESG considerations into volatility forecasting but also contributes to the broader financial literature by demonstrating how advanced statistical models can be adapted to meet the complexities of modern sustainable investments. The findings suggest potential directions for future research, including the integration of real-time ESG data to further refine forecasting accuracy.