Metal losses, including general corrosion, pitting, pinholes, axial groove, lamination, wall thinning, narrow axial, and external corrosion, are common issues observed in oil and gas pipelines. Fortunately, these defects can be detected by employing Pipeline Inspection Gauge (PIG) with the Magnetic Flux Leakage (MFL), which has demonstrated significant efficiency. To do so, the PIG is launched inside the pipelines by the pressure of liquid or gas with the help of launchers and receivers. However, this leads to a lot of background noise mixing with the MFL signals. Since the MFL signals are affected by the PIG speed, it is necessary to find out the function relationship between the filter parameter and the PIG displacement velocity. This paper shall illustrate a data processing procedure implementing adaptive Kalman filtering algorithm to improve the data performance of PIG. In experiments, we have discovered that adjusting the PIG's speed can enhance the performance of the Kalman filter's covariance process, improving the clarity of the MFL signals. The results indicate that by tweaking the PIG's speed within the range of 0.2–3 m/s, the output data from the filter becomes not only clearer but also experiences reduced delay and amplitude reduction as the PIG moves.