In recent years, the incidence of stroke has significantly increased, posing a serious threat to public health. Accurate stroke lesion segmentation techniques can assist physicians in promptly formulating appropriate treatment plans based on specific patient conditions, significantly reducing the risk of disability and mortality, thereby improving patient outcomes. Against this backdrop, leveraging its powerful representation and reasoning capabilities, deep learning has emerged as a key research direction in the field of medical image processing. However, deep learning-based stroke lesion segmentation methods rely on a large amount of precisely labeled medical imaging data, the acquisition of which often faces challenges such as high costs, insufficient quantities, and time-intensive efforts. Traditional data augmentation methods provide some relief but are still limited by data distribution and diversity constraints. Addressing these issues, this paper introduces a novel data generation model, Stroke-CVAE-CGAN, which generates “synthetic lesion masks” based on the spatial distribution characteristics of stroke lesions, serving as constraints for Conditional Generative Adversarial Networks (CGANs), thus enabling the augmented training data that aligns with the distribution patterns of real stroke lesions. Experiments conducted on the ATLAS stroke lesion segmentation dataset show that the augmented data generated by Stroke-CVAE-CGAN closely matches the training data in terms of distribution and exhibits superior Frechet Inception Distance (FID) quality. Utilizing this augmented data to train the U-Net segmentation model significantly enhances the accuracy of stroke lesion segmentation.