ABSTRACT The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into the intergalactic medium, resulting in faint nebular emission lines. We present a machine learning framework for identifying cases of very high hydrogen-ionizing photon escape from galaxies based on the data quality expected from potential NIRSpec observations of EoR galaxies in lensed fields. We train our algorithm on mock samples of JWST/NIRSpec data for galaxies at redshifts z = 6–10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for z ≳ 6 objects of a brightness similar to EoR galaxy candidates uncovered in Frontier Fields observations of galaxy cluster Abell-2744 and MACS-J0416. We find that ionizing escape fractions (fesc) of galaxies brighter than mAB,1500 ≈ 27 mag may be retrieved with mean absolute error Δfesc ≈ 0.09(0.12) for 24 h (1.5 h) JWST/NIRSpec exposures at resolution R = 100. For 24 h exposure time, even fainter galaxies (mAB,1500 < 28.5 mag) can be processed with Δfesc ≈ 0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24 (mAB,1500 < 28.5 mag) and 1.5 h (mAB,1500 < 27 mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high fesc and present the conditions required for detecting a subpopulation of high-fesc galaxies within the data set.