  In recent years, machine learning (ML) algorithms have been successfully
employed in Astronomy for analyzing and interpreting the data collected from
various surveys. The need for new robust and efficient data analysis tools in
Astronomy is imminently growing as we enter the new decade. Astronomical data
sets are growing both in size and complexity at an exponential rate and ML
methodologies can revolutionize our ability to interpret observations and
provide new means of discovery. In this essay we focus on recent success of ML
algorithms in predicting the dynamical mass of galaxy clusters. We discuss the
results of the study performed by Ho et al. [1] and their implications, where
it was found that ML algorithms outperform conventional statistical methods and
can offer a robust and accurate tool for dynamical mass estimation.
