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 softwa-
re 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 mo-
dels, 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.