This　study　developed　and　evaluated　an　automatic　segmentation　model　based　on　the　Mamba　framework　(AM-UNet)　for　rapid　and　precise　delineation　of　high-risk　clinical　target　volume　(HRCTV)　and　organs　at　risk　(OARs)　in　cervical　cancer　brachytherapy.　Using　694　CT　scans　from　179　cervical　cancer　patients,　the　performance　of　five　models　(AM-UNet,　UNet,　DeepLab　V3,　UNETR　and　nnU-Net)　was　compared.　The　models　were　assessed　using　the　Dice　similarity　coefficient　(DSC),　95%　Hausdorff　distance　(HD95),　and　dose-volume　index　(DVI).　AM-UNet　achieved　mean　DSCs　of　0.862,　0.937,　0.823,　and　0.725　for　HRCTV,　bladder,　rectum,　and　sigmoid,　respectively.　Subjective　evaluations　showed　93.07%　of　AM-UNet　predicted　HRCTV　were　rated　as　clinically　acceptable　or　needing　minor　adjustments,　with　no　unacceptable　cases.　Dosimetric　differences　between　AM-UNet-generated　and　manually　delineated　contours　were　within　1%,　highlighting　its　potential　for　improving　clinical　workflows　in　brachytherapy.