Single-atom catalysts (SACs) have emerged as frontier materials in heterogeneous catalysis, offering maximum atom utilization and desirable catalytic performances. However, due to the vast combinatorial space of metal centers, coordination configurations, and host materials, the rational design and precise synthesis of SACs remain challenging, traditionally relying on resource-intensive, trial-and-error experimentation. To overcome this, we present a closed-loop framework that leverages computation-derived design principles and physicochemical insights to guide the targeted design of high-performance SACs. By employing high-throughput density functional theory (DFT) calculations and machine learning, we systematically evaluated SAC systems across various transition metal centers and coordination configurations. By mapping the thermodynamic stability, localized electronic structures and adsorption free energies of key reaction intermediates, we uncovered how coordination numbers and metal–support interactions dictate catalytic properties. This enabled us to identify active moieties within SACs and establish predictive structure-activity relationships for targeted electrocatalytic processes, including the oxygen reduction, nitrate reduction and carbon dioxide reduction. Guided by theoretical predictions, we developed tailored synthetic strategies to experimentally fabricate a series of computationally designed SACs. Advanced characterizations, including aberration-corrected electron microscopy and X-ray absorption spectroscopy, confirmed the atomic dispersion and local coordination of the synthesized SACs. Electrochemical evaluations demonstrated the superior catalytic activity and high Faradaic efficiency of the synthesized SACs. Ultimately, this synergistic theoretical-experimental approach not only elucidates the fundamental reaction mechanisms at the atomic level but also establishes a robust, predictive paradigm. This methodology dramatically accelerates the discovery and optimization of next-generation catalyst materials for clean energy technologies.