  The discovery of the optical counterpart, along with the gravitational waves
from GW170817, of the first binary neutron star merger, opened up a new era for
multi-messenger astrophysics. Combining the GW data with the optical
counterpart, also known as AT2017gfo, classified as a kilonova, has revealed
the nature of compact binary merging systems by extracting enriched information
about the total binary mass, the mass ratio, the system geometry, and the
equation of state. Even though the detection of kilonova brought about a
revolution in the domain of multi-messenger astronomy, since there has been
only one kilonova from a gravitational wave detected binary neutron star merger
event so far, this limits the exact understanding of the origin and propagation
of the kilonova. Here, we use a conditional variational autoencoder trained on
light curve data from two kilonova models having different temporal lengths,
and consequently, generate kilonova light curves rapidly based on physical
parameters of our choice with good accuracy. Once trained, the time scale for
light curve generation is of the order of a few milliseconds, thus speeding up
generating light curves by $1000$ times compared to the simulation. The mean
squared error between the generated and original light curves is typically
$0.015$ with a maximum of $0.08$ for each set of considered physical parameter;
while having a maximum of $\approx0.6$ error across the whole parameter space.
Hence, implementing this technique provides fast and reliably accurate results.
