The laying of the underground pipeline in the same ditch has caused great challenges to the attractive transportation mode of hydrogen mixed with natural gas pipeline in service. The tendency to damage of hydrogen to steel increases the possibility of flammable and explosive gas entering underground engineering significantly. A leakage monitoring method for buried hydrogen-doped natural gas pipeline based on vibration signals with machine learning is proposed. Firstly, the distributed vibration sensor captures the multisource vibration signals propagating in the soil. An optimal combination of wavelet basis functions, decomposition level, and threshold parameters is selected carefully for signal denoising and accurate extraction of leakage-generated signals. Then the characteristics extracted in different frequency bands are investigated with other influencing factors, including the hydrogen-doping ratio, which affects the propagation speed of the pressure wave. The unique characteristics of vibration signal generated by pipeline leakage are extracted. On this basis, combined with the high efficiency of machine learning recognition model, a leakage monitoring model for buried hydrogen-doped natural gas pipeline is established, which achieves a 2.01 % false alarm rate at a maximum positioning distance of 70 cm. It has been successfully applied to the leak detection and location of buried hydrogen-doped natural gas pipelines, which can significantly improve the safety and reliability of underground pipeline system engineering.