Abstract: The machine-learning approach was investigated to predict the mechanical properties of Cu–Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties. Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors. The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel (SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu–Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu–12Al–6Ni alloy with a tensile strength (390 MPa) and hardness (HB 139) that reached the expected values was developed.