Nowadays, morphing aerofoil stands as a valuable configuration to accomplish the required cost reduction and greening objectives. Nevertheless, due to the numerous evaluations needed for the design of these morphing architectures, gradient and CFD-based ASO is prohibitive for these optimisations. Consequently, the state-of-the-art implements surrogate-based optimisation frameworks to strike a balance between accuracy and efficiency. However data-driven optimisation still encounters challenges in generalisation. Indeed, given the complexity of morphing architectures, their development is recommended for a mission profile comprising several flight conditions. This forces the researchers to constrain both the flight envelope and the design space of interest. It is noteworthy that while this approach enhances performance, the generalisation and applicability of the model are reduced.