<p>Ensuring the integrity and credibility of healthcare image data is critical, as any unauthorised modification may lead to severe consequences. Self-embedding watermarking has emerged as an effective signal processing technique aimed at tamper detection and image recovery, particularly in IoT-driven networks. However, many existing block-based medical image watermarking approaches suffer from degraded image quality and limited recovery performance under severe attacks. In this paper, we propose SEWFIR, a secure self-embedding watermarking framework for image recovery. The watermark is generated directly from the host image using Singular Value Decomposition (SVD)-based feature extraction combined with a Normalized Sum (NS) strategy. For robust error detection and correction, a joint Bose–Chaudhuri–Hocquenghem (BCH) and Cyclic Redundancy Check (CRC) coding scheme is employed. Security over IoT-driven networks is further enhanced using Chen’s chaotic system combined with a pseudorandom sequence generator exhibiting strong chaotic behavior. Experimental evaluation demonstrates that the proposed framework attains an average PSNR exceeding 51 dB for watermarked images, while recovered images achieve PSNR values ranging from 25.70 dB to 37.74 dB, outperforming existing methods under various attack scenarios. Additionally, the proposed framework exhibits reduced computational complexity, making it appropriate for practical IoT-based healthcare imaging applications. Unlike deep learning-based watermarking approaches, the proposed framework provides a lightweight and training-free solution suitable for resource-constrained environments.</p>