<p>A style change detection (SCD) task finds the locations of writing style changes within multi-authored documents. In 2017, the SCD task was introduced to assist in various applications, including cybercrime and literary analysis. This paper examines several document representation and prediction methods, including statistical, deep neural network (DNN), classical machine learning (ML), and hybrid methods. An analysis of existing datasets and the performance of SCD solutions is also included in this review. The findings demonstrate that the best method for attaining high performance is supervised ML, especially feed-forward neural networks with pretrained-based representations. Even though DNN models work well for other tasks, they are less frequently developed for this task, and their results need to be improved. This study details a set of challenges that are related to selecting features and adopting pretrained models. Additionally, the available literature is fairly limited in this task compared to others. For further investigation, several research directions are highlighted, including the design of SCD datasets with more realistic styles and the exploration of various learning techniques. This work encourages researchers to foster the growth and development of this research area.</p>