A new point cloud plane fitting method based on an optimized random sampling consensus (RANSAC) algorithm is proposed to improve the flatness detection accuracy of the inner side for high-speed railway wheel pair. In this study, RANSAC algorithm is optimized, and the improved optimization sparrow algorithm (SSA) is adopted. Combined with adaptive inertia weight adjustment, position update, mutation operation, and dynamic randomness reduction, the robustness and accuracy of plane fitting are enhanced. By introducing different noise ratios into the point cloud data and comparing with traditional plane fitting methods, experimental results show that for the plane fitting of the inner side of the wheel pair with 50% noise points addition, when compared with RANSAC and SSA-RANSAC algorithms, the flatness detection accuracy is improved by 93.97% and 57.58%, respectively. Furthermore, the standard deviation is reduced by 4.55% and 28.81%, respectively. These experimental results show that the optimized RANSAC algorithm can significantly improve the fitting accuracy and provide a new and reliable method for high-speed railway wheel pair flatness detection.