The images are frequently shared and reused, increasing the risk of unauthorized copying or manipulation. To address this issue, this paper introduces GIM-GANMark, a dependable watermarking approach that blends the Gaussian Iterated Map with a Generative Adversarial Network to safeguard image ownership. The technique conceals identification information within the image’s transformed domain so that it remains invisible during normal viewing yet traceable for authentication. The optimization process guided by GIM improves the embedding strength and placement of the watermark, while the GAN component enhances the reconstruction accuracy during retrieval. Through this dual mechanism, the hidden data can resist degradations such as compression, noise addition, and minor geometric changes. Experiments on several standard test images confirm that the proposed scheme preserves high visual clarity and yields consistent results in terms of PSNR, SSIM, and NCC when compared with earlier models. Even after mild attacks, the embedded mark can be successfully retrieved, confirming the method’s reliability. Overall, the framework provides a balanced solution that ensures data integrity, ownership verification, and protection of digital images in networked environments.