  Modern radio telescopes will daily generate data sets on the scale of
exabytes for systems like the Square Kilometre Array (SKA). Massive data sets
are a source of unknown and rare astrophysical phenomena that lead to
discoveries. Nonetheless, this is only plausible with the exploitation of
intensive machine intelligence to complement human-aided and traditional
statistical techniques. Recently, there has been a surge in scientific
publications focusing on the use of artificial intelligence in radio astronomy,
addressing challenges such as source extraction, morphological classification,
and anomaly detection. This study presents a succinct, but comprehensive review
of the application of machine intelligence techniques on radio images with
emphasis on the morphological classification of radio galaxies. It aims to
present a detailed synthesis of the relevant papers summarizing the literature
based on data complexity, data pre-processing, and methodological novelty in
radio astronomy. The rapid advancement and application of computer intelligence
in radio astronomy has resulted in a revolution and a new paradigm shift in the
automation of daunting data processes. However, the optimal exploitation of
artificial intelligence in radio astronomy, calls for continued collaborative
efforts in the creation of annotated data sets. Additionally, in order to
quickly locate radio galaxies with similar or dissimilar physical
characteristics, it is necessary to index the identified radio sources.
Nonetheless, this issue has not been adequately addressed in the literature,
making it an open area for further study.
