In this study, the electric field distribution on a single-unit, normal-type (U40 BL) high-voltage ceramic insulator with a 3D geometry was investigated under various pollution levels and applied voltages. The insulator surface was divided into upper and lower regions with conductivities ranging from 10−6 to 10−2 S/m, while the applied voltage varied between 11 and 15 kV. Using COMSOL Multiphysics simulations, electric field values were calculated at eighty critical surface points for all combinations of voltage and surface conductivity. A total of 10,000 electric field data were obtained and classified into five classes based on specific value ranges. These data were then used to train machine learning models including Deep Neural Network, k-Nearest Neighbors, Support Vector Machine, Extreme Gradient Boosting, and Random Forest, with optimal hyperparameters determined through hyperparameter ablation using stratified cross-validation. The comparative analysis of these techniques highlights their respective strengths and weaknesses, demonstrating their effectiveness in accurately predicting the electric field behavior of high-voltage insulators under complex pollution conditions.