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Tumor segmentation in medical imaging is critical for diagnosis, treatment planning, and prognosis, yet remains challenging due to limited annotated data, tumor heterogeneity, and modality-specific complexities in CT, MRI, and histopathology. Although the Segment Anything Model (SAM) shows promise as a zero-shot learner, it struggles with irregular tumor boundaries and domain-specific variations. We introduce the Adaptive Unified Segmentation Anything Model (AUSAM). This novel framework extends SAM’s capabilities for multi-modal tumor segmentation by integrating an intelligent prompt module, dynamic sampling, and stage-based thresholding. Specifically, clustering-based prompt learning (DBSCAN for CT/MRI and K-means for histopathology) adaptively allocates prompts to capture challenging tumor regions, while entropy-guided sampling and dynamic thresholding systematically reduce annotation requirements and computational overhead. Validated on diverse benchmarks—LiTS (CT), FLARE 2023 (CT/MRI), ORCA, and OCDC (histopathology)—AUSAM achieves state-of-the-art Dice Similarity Coefficients (DSC) of 94.25%, 91.84%, 87.59%, and 91.84%, respectively, with significantly reduced data usage. As the first framework to adapt SAM for multi-modal tumor segmentation, AUSAM sets a new standard for precision, scalability, and efficiency. It is offered in two variants: AUSAM-Lite for resource-constrained environments and AUSAM-Max for maximum segmentation accuracy, thereby advancing medical imaging and clinical decision-making.