Machine learning has become increasingly prevalent in daily life. Various machine learning methods are proposed to utilize historical data for making predictions, making people’s life more convenient. However, there is a significant challenge associated with machine learning-privacy leakage. Mere deletion of a user’s data from the training set is not sufficient for avoiding privacy leakage, as the trained model may still harbor this information. To tackle this challenge, the conventional approach entails retraining the model on a new training set that excludes the data of the user. However, this method can be costly, prompting the exploration for a more efficient way to “unlearn” specific data while yielding a model comparable to a retrained one. This study summarizes the current literature on this topic, categorizing existing unlearning methods into three groups: training-based, editing-based, and generation-based methods. Additionally, various metrics are introduced to assess unlearning methods. The study also evaluates current unlearning methods in deep learning and concludes with future research directions in this field.