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.