Protecting medical images in interconnected healthcare systems requires maintaining both diagnostic integrity and secure verification. This study presents a watermarking framework combining deep feature extraction, chaotic cryptography, adaptive frequency-domain embedding, and AI-assisted extraction. Deep convolutional networks identify perceptually tolerant regions for content-aware embedding while preserving vital diagnostic areas. Hybrid chaos-based encryption secures watermark data against unauthorized recovery. The embedding operates in the fractional discrete cosine transform (FDCT) domain with adaptive coefficient selection and multi-objective optimization balancing imperceptibility, robustness, and capacity. During extraction, a convolutional autoencoder refines recovered watermarks, maintaining fidelity under compression, noise, and geometric distortions. Experimental validation on medical datasets demonstrates high embedding capacity (0.07309 BPP), excellent visual similarity (PSNR = 46.85 dB, SSIM = 0.9995), and strong resilience against attacks (NCC ≥ 0.94), ensuring secure and compliant medical image transmission.