AbstractThe Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T_c. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α²F(ω). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α²F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α²F functions and numerical solutions of the Eliashberg equations, we derive a formula for T_c that performs as well as Allen–Dynes for low-T_c superconductors and substantially better for higher-T_c ones. This corrects the systematic underestimation of T_c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.