Undetected fatigue and overload damages at the key locations of the crane boom are among the biggest threats in construction, leading to structural failure. Thus, the structural health of the crane boom should be monitored in real time to ensure that it works under the designed load capacity. In this study, we developed a lightweight digital twin by the multifidelity surrogate (MFS) model to improve the real-time monitoring and prediction accuracy of the structural safety of a crane boom. Digital twin technology, which can establish real-time mapping between the physical space and the digital space, has a promising potential for online monitoring and analysis of structures, equipment, and even human bodies. By combining the MFS model and sensor data, the lightweight digital twin can dynamically mirror the crane boom postures and predict its structural performance in real time. In this study, the structural analysis of the crane boom is limited to the linear elastic stage of materials. Numerical experiments showed that the accuracy of the lightweight digital twin was enhanced compared with that established by the single-fidelity surrogate model, and the computational cost of the lightweight digital twin was decreased with respect to the digital twin built by the numerical method. Meanwhile, the uncertainty from the physical space was analyzed to enhance the reliability of the lightweight digital twin. Thus, the lightweight digital twin developed in our work can ensure accurate safety prediction and design optimization for crane booms.