Accurate segmentation of acute ischemic stroke (AIS) lesions in multimodal magnetic resonance imaging (MRI) is essential for timely clinical intervention but remains challenging due to low lesion contrast, variability across modalities, and the need to capture both local detail and global context. In this work, we propose MHMF‑Diff, a novel diffusion‑based segmentation framework that integrates independent Mamba feature encoders for each MRI sequence (T1, FLAIR, ADC, DWI) with a Hierarchical Global Feature Fusion Block (HGFFB) for cross‑modal information aggregation at multiple decoder levels. During the reverse diffusion process, a U‑Net‑style denoising network is augmented with lightweight Mamba modules that model long‑range dependencies with linear complexity. HGFFB then injects fused, multimodal context into each decoding stage via residual projection. We evaluate MHMF‑Diff on the SOOP dataset comprising 950 stroke cases using the merged lesion label. Our method attains a Dice similarity coefficient (DSC) of 71.64%, a 95th‑percentile Hausdorff distance (HD95) of 18.69 mm, recall of 77.23%, and precision of 73.63%. Compared with the baseline Diff‑UNet, MHMF‑Diff exhibits an increase of 1.93% in DSC, a reduction of 0.63 mm in HD95, a gain of 1.22% in recall, and a gain of 0.81% in precision. These results demonstrate that MHMF‑Diff effectively balances fine‑grained boundary delineation and global anatomical consistency, offering a robust solution for 3D AIS segmentation.