Photonics has been widely explored in computing and communications, mainly to rationalize the relationship between device size minimization and data processing/transmission maximization. Generally driven by optimization and modeling techniques, the design of photonic devices is often performed by bio-inspired algorithms integrated to electromagnetic solvers, which have achieved advances but is still time-consuming. As an alternative to a costly finite element method (FEM) solver, a multilayer perceptron (MLP) neural network is proposed for computing power coupling efficiency of photonic couplers, originally designed through an integrated evolutionary strategy (ES) and FEM routine. We address the ES-FEM design of two efficient couplers, present the MLP implementation and the MLP training and testing over the routine generated datasets, and measure MLP and FEM runtime. MLP suitably predicted the power coupling efficiency of a variety of unknown couplers on tests. The measured runtime showed MLP is ∼105 faster than FEM. In conclusion, MLP is a potential tool to be integrated to ES on the design of such photonic couplers.