<![CDATA[Machine learning has emerged as a promising technological approach for addressing increasingly complex environmental challenges, particularly in the identification and prediction of environmental risks that threaten ecological sustainability and community resilience. Despite the growing availability of environmental data, accurately forecasting potential risks and evaluating resilience capacity remain significant challenges for policymakers and environmental managers. Therefore, this study aims to investigate the application of machine learning techniques for predicting environmental risks and assessing resilience factors that support sustainable environmental management and disaster preparedness. To achieve this objective, a quantitative research approach was employed through the development and evaluation of machine learning models using environmental datasets derived from multiple indicators, including climate conditions, land use patterns, ecological variables, and historical environmental events. The proposed framework integrates data preprocessing, feature selection, model training, and predictive analysis to identify patterns associated with environmental vulnerability and resilience. The findings demonstrate that machine learning models are capable of effectively detecting environmental risk patterns and generating reliable predictions that support proactive decision-making. Furthermore, the analysis reveals that resilience related indicators play a critical role in improving predictive performance and enhancing the understanding of environmental adaptation mechanisms. The integration of predictive analytics and resilience assessment provides a more comprehensive perspective on environmental risk management. In conclusion, machine learning offers substantial potential for advancing environmental risk prediction and resilience evaluation by enabling data-driven strategies for sustainable environmental governance. These findings contribute to the growing body of knowledge on intelligent environmental management systems and support the development of more adaptive and resilient environmental policies.]]>
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