  As a primary step in mineral exploration, a variety of features are mapped
such as lithological units, alteration types, structures, and minerals. These
features are extracted to aid decision-making in targeting ore deposits.
Different types of remote sensing data including satellite optical and radar,
airborne, and drone-based data make it possible to overcome problems associated
with mapping these important parameters on the field. The rapid increase in the
volume of remote sensing data obtained from different platforms has allowed
scientists to develop advanced, innovative, and powerful data processing
methodologies. Machine learning methods can help in processing a wide range of
remote sensing data and in determining the relationship between the reflectance
continuum and features of interest. Moreover, these methods are robust in
processing spectral and ground truth measurements against noise and
uncertainties. In recent years, many studies have been carried out by
supplementing geological surveys with remote sensing data, and this area is now
considered a hotspot in geoscience research. This paper reviews the
implementation and adaptation of some popular and recently established machine
learning methods for remote sensing data processing and investigates their
applications for exploring different ore deposits. Lastly, the challenges and
future directions in this critical interdisciplinary field are discussed.
