The technological advancements of the last several decades has far  outpaced that of centuries prior, nothing more so than the introduction  of the computer which has revolutionized fields the world over,  including scientific research. From this innovation, tools including the  varieties of software programs, modeling of phenomena, automation of  repetitive tasks, and ever more capabilities have sprouted. One of the  more recent additions to the researcher’s toolkit has been introduced  through the growing field of machine learning. Now it is possible to  perform concrete data analysis on what previously would have been  impossibly large datasets, that would have required painstakingly large  amounts of labor. Unlike algorithms that may simply attempt to try and  categorize data according to a strict and inadequate criterion, machine  learning models can identify patterns and form novel organizations that  in the past only humans could do. In many cases, it can actually surpass  human limitations and see patterns that could be missed by  professionals. Within machine learning though, there are as many models  as there are things to be studied. In the biological sciences, some  models are far more useful than others, and this holds even more true as  we narrow down to the fields of epigenetics, where the traits of  organisms are expressed through factors other than genetic code, and  further into neuroepigenetics, where it is focused more specifically on  the brain. The models that will be discussed are the ones most pertinent  to epigenetics in general, but examples of methodologies used  specifically in neuroepigenetic studies will also be evaluated.