Discovered in the 19th century, liquid crystals have only grown more prominent in the last thirty to forty years as a result of electric fields being able to influence their nematic directors. Since these nematic directors can polarize light, liquid crystals have found extensive uses in the field of optoelectronics, including being in many of the devices (such as computers, phones, and tablets) that we have today. With the continued desire to improve these optoelectronic devices comes the desire to improve the liquid crystal systems that help to compose them. The growth of machine learning technologies has been near the forefront of these efforts to improve the efficiency and effectiveness of the development of these systems. In an effort to recognize this prominence, this article presents a review of machine learning’s presence and use in the study of liquid crystals, as well as a comparison of machine learning techniques and more traditional experimental methods.