  This paper presents a machine learning-based method for the detection of the
unique gravitational microlensing signatures of extended dark objects, such as
boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine
learning-based package tailored to handle the challenges posed by low-cadence
data in microlensing surveys. Using realistic observational timestamps, our
models are trained on simulated light curves to distinguish between
microlensing by point-like and extended lenses, as well as from other object
classes which give a variable magnitude. We show that boson stars, examples of
objects with a relatively flat mass distribution, can be confidently identified
for $0.8 \lesssim r/r_E\lesssim 3$. Intriguingly, we also find that more
sharply peaked structures, such as NFW-subhalos, can be distinctly recognized
from point-lenses under regular observation cadence. Our findings significantly
advance the potential of microlensing data in uncovering the elusive nature of
extended dark objects. The code and dataset used are also provided.
