With the growing reliance on telemedicine, ensuring the authenticity and confidentiality of medical images has become a critical challenge. This study presents a blind watermarking framework for medical images that balances imperceptibility, robustness, and computational efficiency. The proposed method combines Dual-Tree Complex Wavelet Transform (DT-CWT) for multi-directional decomposition, QR decomposition for numerically stable embedding, and Glowworm Swarm Optimization (GSO) for adaptive coefficient selection. To maintain visual fidelity, watermark insertion is restricted to the luminance channel and guided by a Human Visual System (HVS)-based quantization strategy. The embedded payload includes both compressed patient metadata and a biometric facial image, protected by BCH error correction and lightweight encryption. Experimental evaluations on a public dataset of RGB CT and X-ray images demonstrate strong imperceptibility (PSNR 44.67 dB, SSIM 0.9853), which is competitive with published values for comparable watermarking methods. The scheme achieves high robustness under noise, filtering, compression, and moderate geometric distortions (NCC ≥ 0.879), with competitive or improved robustness metrics (up to 15.3% higher NCC under certain attacks compared to published results), while maintaining efficient processing (1.32 s embedding, 1.08 s extraction per image). These results confirm the method’s suitability for real-world telemedicine applications requiring secure and visually intact medical image transmission.