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We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We identify several metallic candidates with predicted high conductivities comparable to that of aluminum (36.59 × 106 S/m). We perform full electron–phonon coupling calculations for the top-performing materials, yielding results in good agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) possess a conductivity that remains difficult to surpass due to their unique electronic structure and low scattering, compounds like LiBePt2 can achieve good performance by utilizing valence electrons from light elements to shift high-scattering d-states beneath the Fermi level. This study not only identifies novel high-performance conductors but also demonstrates the predictive power of combining statistical learning with detailed ab initio calculations.