Recent advances in machine learning have led to a surge of interest in classification of the auditory brainstem response. By conducting a search in the PubMed, Google Scholar, SpringerLink, ScienceDirect, and Scopus databases, it was possible to identify twelve studies that explored the use of machine learning to classify the auditory brainstem response as a complementary and objective method to (a) help clinicians better diagnose hearing impairment by discerning between healthy and pathological auditory brainstem response waveforms, (b) present a neural marker for potential applications in hearing aid tuning, and (c) provide a biometric marker for discriminating between subjects. A comparison between the studies presented in this review is not possible as they used different test subjects, group sizes, and stimuli, and evaluated ABR differently. Instead, the result of these studies will be presented and their limitations as well as their potential applications will be discussed. Overall, the findings of these studies suggest that ABR classification using machine learning is a promising tool for assessing patients with hearing loss, optimizing technologies for tuning hearing aids, and discriminating between subjects.