Herein, laser milling of Al₂O₃ ceramics is conducted using nanosecond pulsed laser. The relationship among milling process parameters, surface roughness, and milling depth is investigated using response surface second-order regression model. In addition, the key process parameters affecting surface roughness and milling depth are identified using sensitivity analysis. A genetic algorithm is used to determine the optimal process parameters that minimize the surface roughness and maximize the milling depth. These parameters are then experimentally verified. Results indicate a strong predictive ability of the mathematical model based on the response surface method. The number of milling times and overlap rate have the most significant effect on the surface roughness and milling depth. Under optimized parameters, the predicted surface roughness and milling depth are found to be 10.471 μm and 120.526 μm, respectively, while their corresponding experimental values are 10.835 μm and 131.277 μm, respectively. Therefore, the relative errors in case of surface roughness and milling depth are only 3.36% and 8.19%, respectively.