Cardiac Magnetic Resonance Imaging (CMR) is a crucial clinical imaging modality for assessing cardiac morphology and function, and it has become the gold standard for diagnosing cardiovascular diseases. However, its widespread clinical adoption is hindered by long acquisition times and high costs—challenges that are particularly acute in dynamic imaging, where both high spatial and temporal resolutions must be achieved within a limited timeframe. In this paper, we propose a novel dynamic deep unrolling method, CMambaR, a cardiac phase-embedded Vision Mamba architecture designed to accelerate cardiac MRI reconstruction. The proposed method integrates the strengths of unfolded iterative optimization with a spatiotemporal dynamic reconstruction network, enabling it to effectively capture complementary information embedded in dynamic sequences while leveraging physics-based priors to deliver high-quality reconstruction. Inspired by structured state space models, we design a local enhanced vision Mamba module as the core building block of our network, capable of capturing both local details and long-range dependencies. Furthermore, we introduce a cardiac phase fusion mechanism that incorporates cardiac phase prior into the reconstruction process, further enhancing reconstruction performance. Extensive experiments on two cardiac datasets demonstrate that our method achieves high-fidelity image reconstruction and consistently outperforms existing approaches.