This paper proposes a volumetric blind watermarking framework for 3D medical images based on adaptive DWT–SVD embedding with multi-slice fusion. The method dynamically adjusts embedding strength per slice using two complementary criteria: entropy (to exploit perceptual masking in textured regions) and anatomical position (to prioritize centrally located slices that are less likely to be cropped). A content-dependent chaotic encryption, initialized from the SHA-256 hash of the entire volume, secures the watermark before embedding into the low-frequency DWT subbands via singular value modification. During extraction, a multi-slice fusion mechanism aggregates watermark estimates from all slices using positional weights, ensuring robust recovery even under localized attacks. Experiments on 20 brain MRI and 15 chest CT volumes (256×256× up to 160 slices) demonstrate high imperceptibility (average PSNR = 44.8dB for MRI, 43.6dB for CT; SSIM >0.997) and strong robustness (average BER = 0.044 across 15 distinct attack types including JPEG compression, Gaussian noise, median filtering, rotation, scaling, cropping, and slice drop-out). Compared to nine state-of-the-art methods, including transform-based, feature-based, and deep learning approaches, the proposed framework achieves the lowest average BER (0.044 versus 0.063 for the closest competitor MCANet) while maintaining competitive imperceptibility and requiring only CPU-based computation (≈1.3 seconds per volume). These results position the proposed method as a practical, secure, and clinically viable solution for protecting patient data in telemedicine and PACS environments.