A novel modeling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters (IP) are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3-octave bands, proving the ability of the model to generalize. A metaheuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selected pairs of target values. This is particularly important for reconciling opposing trends, such as the noise reduction capability and aerodynamic performance. The chosen optimization strategy also allows for a customized design of serrated leading edges, tailored to the specific operating conditions of the axial fan.