MedM2G is a unified Medical Multi-Modal Generative framework designed to align, extract, and generate multiple medical modalities within a single model. The key innovations include a central alignment approach to efficiently align multiple medical modalities in a unified space, a medical visual invariant preservation method to maintain specific clinical knowledge, and a latent multi-flow cross-guided diffusion process to enhance cross-modal interactions. MedM2G addresses the challenges of aligning and generating multiple medical modalities, such as CT, MRI, and X-ray, by leveraging a unified diffusion model. Extensive experiments on 5 medical multi-modal generation tasks across 10 datasets demonstrate that MedM2G outperforms various state-of-the-art models, achieving superior results in text-to-image, image-to-text, and multi-modal generation tasks. The framework's effectiveness is validated through comprehensive evaluations, including quantitative metrics and qualitative analysis, showcasing its ability to generate high-quality medical images and improve downstream tasks like classification, segmentation, and detection.MedM2G is a unified Medical Multi-Modal Generative framework designed to align, extract, and generate multiple medical modalities within a single model. The key innovations include a central alignment approach to efficiently align multiple medical modalities in a unified space, a medical visual invariant preservation method to maintain specific clinical knowledge, and a latent multi-flow cross-guided diffusion process to enhance cross-modal interactions. MedM2G addresses the challenges of aligning and generating multiple medical modalities, such as CT, MRI, and X-ray, by leveraging a unified diffusion model. Extensive experiments on 5 medical multi-modal generation tasks across 10 datasets demonstrate that MedM2G outperforms various state-of-the-art models, achieving superior results in text-to-image, image-to-text, and multi-modal generation tasks. The framework's effectiveness is validated through comprehensive evaluations, including quantitative metrics and qualitative analysis, showcasing its ability to generate high-quality medical images and improve downstream tasks like classification, segmentation, and detection.