Abstract: The mutual restriction between mechanical strength and electrical conductivity (EC) in precipitation-strengthened copper alloys designed for high-end manufacturing has long been a major challenge in materials science. The pursuit of an enhanced comprehensive performance, in which both properties are simultaneously improved, remains a key challenge in alloy development. In this study, machine learning (ML) techniques were employed for elemental feature selection to uncover the key physicochemical descriptors that govern the performance of precipitation-hardened Cu alloys. This approach enables the rational design of high-performance multi-component and compositionally complex alloys. By integrating correlation-based screening, recursive feature elimination, and exhaustive search methods, this study systematically identified five critical alloying features that dominate hardness and five others that govern the electrical conductivity of age-hardened copper alloys. The five descriptors identified as having the most significant impact on the alloy hardness were M-C10, M-S12, V-A8, M-S8, and O12. These factors influence the final mechanical strength by affecting the efficiency of solid solution strengthening and precipitation hardening mechanisms, which directly impede dislocation motion and plastic deformation. Five key features, namely M-A10, M-E4, M-C4, V-S11, and V-E6, were identified as the dominant determinants of electrical conductivity. These features are related to the regulation of free electron concentration, adjustment of electron mean free path, and extent of electron scattering caused by solute atoms and microstructural inhomogeneities. In particular, descriptors such as M-A10 and M-E4 capture the electronic configuration and bonding characteristics of the alloying elements, whereas V-S11 and V-E6 are associated with the valence electron behavior and energy level distribution, all of which significantly impact the mobility of conduction electrons within an alloy matrix. To quantitatively model the relationships between the alloying features and material properties, support vector regression (SVR) models were constructed using the selected descriptors as inputs. A grid search method was applied to optimize hyperparameters, yielding predictive models with high accuracy. Specifically, the SVR model for hardness prediction achieved a relative error less than 6%, and the model for electrical conductivity prediction achieved an error below 5%, demonstrating the reliability of the selected features and feasibility of ML-based property prediction. Considering factors such as sustainability, cost efficiency, and the feasibility of large-scale industrial production, the design space for alloy composition was further constrained. The number of alloying elements was reduced, and expensive or scarce elements were limited; specifically, the cobalt content was restricted to below 1% to ensure both economic and environmental sustainability. Within this constrained compositional space, predictive models were used to evaluate candidate alloys. A novel alloy composition, Cu–2.92Ni–0.92Co–0.74Si, was proposed based on its predicted optimal combination of mechanical and electrical performance. Experimental validation was performed following established industrial processing routes for Cu–Ni–Co–Si alloys. The fabricated alloy demonstrated an ultimate tensile strength of 868 MPa and electrical conductivity of 45.6% IACS (International annealing copper standard), signifying a successful breakthrough in the concurrent enhancement of traditionally antagonistic properties. Microstructural characterization revealed that the superior combined performance was primarily due to the formation of a high density of fine and uniformly dispersed precipitates. These precipitates not only contributed to significant strengthening by hindering dislocation movement, but also decreased the concentration of solute atoms in the matrix, thereby reducing electron scattering and enhancing conductivity. In addition, strain hardening introduced during thermomechanical processing contributed to an improvement in mechanical strength. In conclusion, this study demonstrates that by combining machine learning-based predictive modeling with experimental validation, it is possible to design precipitation-strengthened copper alloys that simultaneously exhibit high mechanical strength and electrical conductivity. This study provides a robust and scalable methodology for the intelligent design of advanced copper-based materials tailored for high-performance manufacturing applications.