Abstract: Resilient urban power grids, while showcasing remarkable adaptability to diverse disturbances and disasters, pose significant challenges in risk warning due to their complexity. This underscores the necessity of integrating advanced technologies such as big data and machine learning. This study proposes a novel approach to resolve these issues. First, a resilient urban power grid risk assessment index system was established, employing a comprehensive weighted approach that combined subjective and objective factors to weigh the indicators. Leveraging real-time data flow obtained through big data technology, dynamic weights for risk assessment indicators were determined. Subsequently, a resilient urban power grid risk assessment standard cloud was developed, and the membership degree of the resilient urban power grid risk level was computed to ascertain the risk level. Finally, a resilient urban power grid risk warning model was formulated using random forest, and a thorough numerical analysis was conducted. Compared with other models, the constructed model exhibited high precision characteristics. The findings demonstrated that the developed model exerted a substantial risk-warning effect, enabling timely implementation of effective risk control measures to ensure the stable operation of resilient urban power grids.