  Machine learning is a powerful technique, becoming increasingly popular in
astrophysics. In this paper, we apply machine learning to more than a thousand
globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I'
project in order to correlate present-day observable properties with the
presence of a subsystem of stellar mass black holes (BHs). The machine learning
model is then applied to available observed parameters for Galactic GCs to
identify which of them that are most likely to be hosting a sizeable number of
BHs and reveal insights into what properties lead to the formation of BH
subsystems. With our machine learning model, we were able to shortlist 21
Galactic GCs that are most likely to contain a BH subsystem. We show that the
clusters shortlisted by the machine learning classifier include those in which
BH candidates have been observed (M22, M10 and NGC 3201) and that our results
line up well with independent simulations and previous studies that manually
compared simulated GC models with observed properties of Galactic GCs. These
results can be useful for observers searching for elusive stellar mass BH
candidates in GCs and further our understanding of the role BHs play in GC
evolution. In addition, we have released an online tool that allows one to get
predictions from our model after they input observable properties.
