A neural network is constructed to describe flow stress curves for the AlMg6/10% SiC metal matrix composite (MMC) at temperatures ranging from 300 to 500 °С and strain rates ranging from 0.1 to 5 s−1. The metal matrix composite was produced by powder metallurgy technologies from the AlMg6 alloy (the 1560 aluminum alloy according to GOST 4784-97) with the addition of 10% SiC powder having a fraction of F1500. The obtained flow stress curves of the AlMg6/10% SiC MMC in the studied temperature–rate range have several sections (stages). At the first stage, the material hardens, then it softens, and a peak of deformation stress appears on the flow stress curve. After the softening stage, a steady section begins, at which the hardening and softening rates almost coincide. This is expressed in the constancy of the flow stress value with an increase in strain. The obtained flow stress curves are described by a neural network. The study shows that the constructed neural network can predict with acceptable engineering accuracy the behavior of flow stress curves for temperatures and strain rates that are not used in its training.