  Cybercrime is a growing threat to organizations and individuals worldwide,
with criminals using sophisticated techniques to breach security systems and
steal sensitive data. This paper aims to comprehensively survey the latest
advancements in cybercrime prediction, highlighting the relevant research. For
this purpose, we reviewed more than 150 research articles and discussed 50 most
recent and appropriate ones. We start the review with some standard methods
cybercriminals use and then focus on the latest machine and deep learning
techniques, which detect anomalous behavior and identify potential threats. We
also discuss transfer learning, which allows models trained on one dataset to
be adapted for use on another dataset. We then focus on active and
reinforcement learning as part of early-stage algorithmic research in
cybercrime prediction. Finally, we discuss critical innovations, research gaps,
and future research opportunities in Cybercrime prediction. This paper presents
a holistic view of cutting-edge developments and publicly available datasets.
