  Development and exploitation of technology have led to the further expansion
and complexity of digital crimes. On the other hand, the growing volume of data
and, subsequently, evidence is a severe challenge in digital forensics. In
recent years, the application of machine learning techniques to identify and
analyze evidence has been on the rise in different digital forensics domains.
This paper offers a systematic literature review of the research published in
major academic databases from January 2010 to December 2021 on the application
of machine learning in digital forensics, which was not presented yet to the
best of our knowledge as comprehensive as this. The review also identifies the
domains of digital forensics and machine learning methods that have received
the most attention in the previous papers and finally introduces remaining
research gaps. Our findings demonstrate that image forensics has obtained the
greatest benefit from using machine learning methods, compared to other
forensic domains. Moreover, CNN-based models are the most important machine
learning methods that are increasingly being used in digital forensics. We
present a comprehensive mind map to provide a proper perspective for valuable
analytical results. Furthermore, visual analysis has been conducted based on
the keywords of the papers, providing different thematic relevance topics. This
research will give digital forensics investigators, machine learning
developers, security researchers, and enthusiasts a broad view of the
application of machine learning in digital forensics.
