This study investigates the development and application of meta-models for crashworthiness assessment of helicopter structures and components. It aims to address the challenges associated with scarcity of data from computationally expensive simulations and experimental drop-tests, and enable the use of surrogates in a crashworthiness optimization framework. Two predictive approaches utilizing Machine Learning techniques are compared to predict and assess the energy absorption of tubular metallic structures for different cross-section configurations. The first approach directly predicts energy absorption, while the second predicts load-displacement curves, from which energy absorption is derived. Results indicate that certain regressors, such as the Transform Target Regressor, the Decision Tree Regressor and the Poisson Regressor, consistently achieve high accuracy in predicting load-displacement curves and energy absorption across the evaluated tubular samples. A low-fidelity model able to provide less accurate but computationally inexpensive information is then introduced. The influence of low-fidelity data is investigated when it serves as additional input alongside high-fidelity data during the training phase of the surrogate model, through a comparative analysis. The research's findings suggest the efficiency of Machine Learning in representing structural behaviour under crushing conditions and highlight the potential for further enhancements through the integration of low-fidelity data, thereby holding promise for extending the methodology to more complex structures.