The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET) is introduced. This is a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI accelerated discovery pipeline that incorporates elemental-substitution strategies and machine learned interatomic potentials, this workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T_c > 5 K. The successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds was reported. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.