The existing models and methods used to determine the melting temperature of the mold powders used in the
continuous casting process remain inaccurate in the case of equations reported in current literature or consider
for commercial software only an equilibrium state. In this work, a new approach has been implemented using
neural networks, which will act as a “black box” to predict the melting temperature of a mold powder with a
given composition within an acceptable range. The proposed neural network will be working as a regression
neural model whose inputs will be the composition of each of the chemical species contained within the
powder. A database provided by a research net comprising multiple countries' research institutes will be fed
as a training set for network learning. Such data comes from experimental measurements performed mainly
through the high-temperature microscope test. The correct implementation and training of the network should
provide a new alternative to develop new products and verify existing products' melting properties. In future
models, further considerations should be made towards a better understanding of these phenomena, which
should consider factors such as the formation of mineral phases, interaction among some specific components
of the powder, or even the parameters used at the time of experimental measurement.