  Strong-gravitationally lensed supernovae (LSNe) are promising probes for
providing absolute distance measurements using gravitational lens time delays.
Spatially unresolved LSNe offer an opportunity to enhance the sample size for
precision cosmology. We predict that there will be approximately $3$ times more
unresolved than resolved LSNe Ia in the Legacy Survey of Space and Time (LSST)
by the Rubin Observatory. In this article, we explore the feasibility of
detecting unresolved LSNe Ia from the shape of the observed blended light
curves using deep learning techniques, and we find that $\sim 30\%$ can be
detected with a simple 1D CNN using well-sampled $rizy$-band light curves (with
a false-positive rate of $\sim 3\%$). Even when the light curve is
well-observed in only a single band among $r$, $i$, and $z$, detection is still
possible with false-positive rates ranging from $\sim 4-7\%$, depending on the
band. Furthermore, we demonstrate that these unresolved cases can be detected
at an early stage using light curves up to $\sim20$ days from the first
observation, with well-controlled false-positive rates, providing ample
opportunities for triggering follow-up observations. Additionally, we
demonstrate the feasibility of time-delay estimations using solely LSST-like
data of unresolved light curves, particularly for doubles, when excluding
systems with low time delay and magnification ratio. However, the abundance of
such systems among those unresolved in LSST poses a significant challenge. This
approach holds potential utility for upcoming wide-field surveys, and overall
results could significantly improve with enhanced cadence and depth in the
future surveys.
