This research focuses on aluminum alloys, including wrought ones. The authors identify the factors affecting their properties like alloying components and external pressure applied to the crystallizing metal. Pressure is selected depending on the level and nature of changes in this parameter over time. This helps significantly affect the state of the alloy and the formation conditions of its properties. The authors used hardness as the output parameter. Deductor software was used to train the neural network model. Learning also relied on the Resilient Propagation (Rprop) algorithm. The authors developed a neural network model comprising an input layer of eleven neurons, an output layer of one neuron, and a hidden layer of seven neurons. The developed ANN meets the conditions and can be used for the forecasting of aluminum alloy hardness depending on the percentage content of chemical elements and applied pressure. The provided solution can be used as a decision support system (DSS) at companies that use pressure molding and liquid forging.