KMeans-SMOTE is a technique used to address class imbalance in datasets, particularly in machine learning. It combines K-Means clustering and SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class. By clustering the minority class data points, it identifies the distribution of these points and creates new samples that are more representative of the underlying data structure. This method enhances the performance of classification algorithms by providing a more balanced dataset. The synthetic samples generated through KMeans-SMOTE help improve model training, leading to better predictions and reducing the risk of overfitting to the majority class.