We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (T<sub>c</sub>) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have T<sub>c</sub> values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 541 compounds with T<sub>c</sub> values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN<sub>2</sub>, which we predict to be superconducting in the stoichiometric trigonal phase, with a T<sub>c</sub> exceeding 38 K. LiMoN<sub>2</sub> has been previously synthesized in this phase, further heightening its potential for practical applications.
