  Machine learning (ML) refers to computer algorithms that predict a meaningful
output or categorize complex systems based on a large amount of data. ML is
applied in various areas including natural science, engineering, space
exploration, and even gaming development. This review focuses on the use of
machine learning in the field of chemical and biological oceanography. In the
prediction of global fixed nitrogen levels, partial carbon dioxide pressure,
and other chemical properties, the application of ML is a promising tool.
Machine learning is also utilized in the field of biological oceanography to
detect planktonic forms from various images (i.e., microscopy, FlowCAM, and
video recorders), spectrometers, and other signal processing techniques.
Moreover, ML successfully classified the mammals using their acoustics,
detecting endangered mammalian and fish species in a specific environment. Most
importantly, using environmental data, the ML proved to be an effective method
for predicting hypoxic conditions and harmful algal bloom events, an essential
measurement in terms of environmental monitoring. Furthermore, machine learning
was used to construct a number of databases for various species that will be
useful to other researchers, and the creation of new algorithms will help the
marine research community better comprehend the chemistry and biology of the
ocean.
