The increased need to design higher performing aerodynamic shapes has led to design optimisation cycles requiring high-fidelity CFD models and high-dimensional parametrisation schemes. The computational cost of employing global search algorithms on such scenarios has typically been prohibitive for most academic and industrial environments. In this paper, a novel strategy is presented that leverages the capabilities of Artificial Neural Networks for regressing complex unstructured data, while coupling them with dimensionality reduction algorithms. This approach enables employing global-based optimisation methods on high-dimensional applications through a reduced computational cost. This methodology is demonstrated on the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. The outcome is compared against a state-of-the-art adjoint-based approach. Results indicate the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost, and can scale better to multi-objective optimisation applications.