Effective Skin Lesion Segmentation is crucial for dermatological care, it enables the early identification and accurate diagnosis of skin cancer. Denoising Diffusion Probabilistic Models (DDPMs) have recently become a major focus in computer vision. Its applications in image generation, such as Stable Diffusion, Latent Diffusion Models and Imagen, have showcased remarkable abilities in creating high-quality generative outputs. Recent research highlights that DDPMs also perform exceptionally well in medical image analysis, specifically in medical image segmentation tasks. Even though a U-Net backbone served as the foundation for these models initially, there is a promising opportunity to boost their performance by incorporating other mechanisms. Recent research include transformer-based framework for diffusion models, but the advancement come with the challenge of inherent quadratic complexity. Research has shown that state space models (SSMs), like Mamba efficiently capture long-range dependencies while maintaining linear computational complexity. Due to these benefits, it outperforms many of the mainstream foundational architectures. However, we found that simply merging Mamba with diffusion results in suboptimal performance. To truly harness the power of these two advanced technologies for medical image segmentation, a more effective integration is required, we formulate a novel Mamba-Based Diffusion framework, called DiffMamba for skin lesion segmentation. We access its performance on the ISIC 2018 dataset for skin lesion segmentation, and our method outperforms existing state-of-the-art techniques. The code is available at: https://github.com/amit-shakya-28/DiffMamba