Knowledge distillation is a model compression technique that enhances the performance and efficiency of a smaller model (student model) by transferring knowledge from a larger model (teacher model). This technique utilizes the outputs of the teacher model, such as soft labels, intermediate features, or attention weights, as additional supervisory signals to guide the learning process of the student model. By doing so, knowledge distillation reduces computational resources and storage space requirements while maintaining or surpassing the accuracy of the teacher model. Research on knowledge distillation has evolved significantly since its inception in the 1980s, especially with the introduction of soft labels by Hinton and colleagues in 2015. Various advancements have been made, including methods to extract richer knowledge, knowledge sharing among models, integration with other compression techniques, and application in diverse domains like natural language processing and reinforcement learning. This article provides a comprehensive review of knowledge distillation, covering its concepts, methods, applications, challenges, and future directions.