  Radio interferometric experiments aim to constrain the reionization model
parameters by measuring the 21-cm signal statistics, primarily the power
spectrum. However the Epoch of Reionization (EoR) 21-cm signal is highly
non-Gaussian, and this non-Gaussianity encodes important information about this
era. The bispectrum is the lowest order statistic able to capture this inherent
non-Gaussianity. Here we are the first to demonstrate that bispectra for large
and intermediate length scales and for all unique $k$-triangle shapes provide
tighter constraints on the EoR parameters compared to the power spectrum or the
bispectra for a limited number of shapes of $k$-triangles. We use the Bayesian
inference technique to constrain EoR parameters. We have also developed an
Artificial Neural Network (ANN) based emulator for the EoR 21-cm power spectrum
and bispectrum which we use to remarkably speed up our parameter inference
pipeline. Here we have considered the sample variance and the system noise
uncertainties corresponding to $1000$ hrs of SKA-Low observations for
estimating errors in the signal statistics. We find that using all unique
$k$-triangle bispectra improves the constraints on parameters by a factor of
$2-4$ (depending on the stage of reionization) over the constraints that are
obtained using power spectrum alone.
