Silicone rubber materials are commonly used as insulation materials under high pressure conditions due to their excellent insulation properties. The breakdown field strength is an important electrical performance index, and there is a complex nonlinear relationship between the breakdown strength and the material formula. Based on this, an efficient evaluation model based on genetic (GA) optimized extreme gradient boosting (XGBoost) algorithm is proposed. The model combines GA and XGBoost, and uses temperature, relative content of masterbatch, diameter of Al(OH)3 micropowder, content and thickness of Al(OH)3 as inputs to establish an XGBoost model to predict the breakdown strength. The GA algorithm automatically selects the optimal parameters during the training process of the XGBoost model. The Pearson correlation coefficient was used to analyze the influencing factors. It can be seen that the thickness and temperature are the key factors affecting the breakdown field strength, while the influence of the masterbatch content, the diameter and content of Al(OH)3 micropowder is relatively small. The commonly used regression model is compared with the evaluation index of the proposed model. The coefficient of determination of the model can reach 0.953, and the root mean square error and mean absolute error are only 0.361 and 0.168, respectively. The results show that the GA-XGBoost model can accurately predict the breakdown strength of the material, which can provide a reference for studying the properties of silicone rubber materials and optimizing the material formulation.