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Abstract We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for active galactic nucleus (AGN) host galaxies withinz < 1.4 andm < 23 in the Hyper Supreme-Cam (HSC) Wide survey. We divide the data into five redshift bins:low(0 < z < 0.25),mid(0.25 < z < 0.5),high(0.5 < z < 0.9),extra(0.9 < z < 1.1), andextreme(1.1 < z < 1.4), and train our models independently in each bin. We use PSFGAN to decompose the AGN point-source light from its host galaxy, and invoke the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters of the recovered host galaxy. We first trained our models on simulated data, and then fine-tuned our algorithm via transfer learning using labeled real data. To create training labels for transfer learning, we used GALFIT to fit  ∼20,000 real HSC galaxies in each redshift bin. We comprehensively examined that the predicted values from our final models agree well with the GALFIT values for the vast majority of cases. Our PSFGAN + GaMPEN framework runs at least three orders of magnitude faster than traditional light-profile fitting methods, and can be easily retrained for other morphological parameters or on other data sets with diverse ranges of resolutions, seeing conditions, and signal-to-noise ratios, making it an ideal tool for analyzing AGN host galaxies from large surveys coming soon from the Rubin-LSST, Euclid, and Roman telescopes.