<p>The spatial offset of bridge has a significant impact on the safety, comfort, and durability of high-speed railway (HSR) operations, so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges. Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality, cost-effectiveness, and efficiency. However, existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source, low detection accuracy, and the inability to identify lateral deformations of bridges. This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train. Firstly, dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals, and explore the hidden temporal relationships in the data. Subsequently, a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge. A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train–track–bridge interaction model, and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated. Finally, the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train. The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge, ensuring the long-term operational safety of HSR bridges.</p>