The concept of digital twins (DT)s enhances the traditional structural health monitoring (SHM) by integrating real-time data with digital models for predictive maintenance and decision-making whilst combined with finite element modelling (FEM). However, the computational demand of FE modelling necessitates surrogate models for real-time performance, alongside the requirement of inverse structural analysis to infer overall behaviour via measured structural response of a structure. A FEM-based machine learning (ML) model is an ideal option in this context as it can be trained to perform those calculations instantly based on FE-based training data. However, the performance of the surrogate model depends on the ML model architecture. In this light, current study investigates three distinct ML models to surrogate FE modelling for DTs. It was identified that all models demonstrated a strong performance, with the tree-based models outperforming the performance of the neural network (NN) model. The highest accuracy of the surrogate model was identified in the random forest (RF) model with an error of 0.000350 whilst the lowest inference time was observed with the trained XGBoost algorithm which was at approximately 1 millisecond. By leveraging the capabilities of ML, FEM and DTs, this study presents an ideal solution for implementing real-time DTs to advance the functionalities of current SHM systems.