  The application of Machine Learning (ML) to hydrologic modeling is fledgling.
Its applicability to capture the dependencies on watersheds to forecast better
within a short period is fascinating. One of the key reasons to adopt ML
algorithms over physics-based models is its computational efficiency advantage
and flexibility to work with various data sets. The diverse applications,
particularly in emergency response and expanding over a large scale, demand the
hydrological model in a short time and make researchers adopt data-driven
modeling approaches unhesitatingly. In this work, in the era of ML and deep
learning (DL), how it can help to improve the overall run time of physics-based
model and potential constraints that should be addressed while modeling. This
paper covers the opportunities and challenges of adopting ML for hydrological
modeling and subsequently how it can help to improve the simulation time of
physics-based models and future works that should be addressed.
