Continual learning addresses the funda- mental challenge of catastrophic forgetting, wherein models significantly decrease in performance on pre- viously learned tasks when acquiring new knowledge. The Rehearsal-based method is an effective approach for mitigating this problem by retraining samples from old tasks while learning new tasks. In this paper, we propose the GMPA model, which uses Gaussian Mixture Prototype Augmentation to generate exemplars from the approximate distribution of previously learned classes for rehearsal during training on new tasks. The GMPA parameters are stored in memory and used to create augmented features proportional to the current task’s class distribution, maintaining a balanced representation between old and new classes. The experimental results on various benchmark datasets, including CIFAR-100, ImageNet-subset, CUB-200, Stanford-cars, and Food-101, demonstrate that our method achieves state-of-the-art performance across various continual learning scenarios.