Camera calibration is essential for various vision-based 3D metrological techniques. In this paper, a novel camera calibration method, to the best of our knowledge, combining synthetic speckle pattern and an improved gray wolf optimizer algorithm is presented. The synthetic speckle pattern serves as the calibration target. The particle swarm algorithm-based digital image correlation is employed to achieve matches among 3D control points and 2D image points; then the improved gray wolf optimizer algorithm is used to calculate the camera parameters. For verification, simulated and real tests are conducted. Through the analysis of calibration results, the proposed method performs better and is more stable than other calibration targets. Research on the influence of camera pose and optimization algorithm is conducted, showing that the improved gray wolf optimizer algorithm performs better than other benchmark algorithms. The camera parameters can be obtained through one captured image when the speckle patterns are added in the portion of the camera sensor.