Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design. Then, three main inverse design methods—exploration-based, model-based, and optimization-based—are analyzed in the context of different application scenarios. Finally, the applications of inverse design methods in alloys, optical materials, and acoustic materials are elaborated on, and the prospects for materials inverse design are discussed. The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.