The increased need to design higher performing aerodynamic shapes has led to design optimization cycles requiring high-fidelity CFD models and high-dimensional parametrization 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 optimization methods on high-dimensional applications through a reduced computational cost. This methodology is demonstrated on the efficiency optimization 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 that the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost and can scale better to multi-objective optimization applications.