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We present a computational framework that integrates machine learning with high-throughput \textit{ab initio} calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum ( ~S/m). We perform full electron--phonon coupling calculations for the top-performing materials, yielding results in excellent agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) define the practical ceiling for conductivity due to their unique electronic structure and low scattering, compounds like can achieve comparable performance by utilizing valence electrons from light elements to shift high-scattering -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.