Abstract: This article provides a systematic review of the theoretical and applied research on measurement uncertainty since the inception of the theory. Firstly, it gives an overall introduction to the historical development of measurement uncertainty theory. Secondly, the basic principles, latest research applications, and limitations of several mainstream measurement uncertainty evaluation methods are summarized. For example, the earliest published GUM method, which is mainly applicable to linear or approximately linear measurement models, adopts the method based on the propagation of standard uncertainty and is currently the most commonly used evaluation method. The Monte Carlo-based measurement uncertainty evaluation method and its derivatives, the quasi-Monte Carlo method and the adaptive Monte Carlo method, have wider applicability when dealing with complex models. The Bayesian-based measurement uncertainty evaluation method can fully exploit the value of prior data in small-sample measurements and has good performance. In addition, the article discusses some non-statistical methods for measurement uncertainty evaluation, such as the grey evaluation method, fuzzy evaluation method, maximum entropy method, and neural network method. Finally, the article briefly summarizes the various evaluation methods and suggests that with the development of artificial intelligence technology, methods such as support vector machines and neural networks have broad prospects for application in complex measurement models and measurement environments.