In today's world, data security and privacy remain as significant challenges. Reversible data hiding (RDH) is a technique used to embed data into cover media in a manner that allows both the embedded data and the original media to be recovered. Unlike traditional data hiding techniques, which permanently alter the cover media, RDH ensures that the original content can be restored exactly as it was before embedding. The concept of RDH relies on the fact that the embedded data does not cause irreversible distortion or permanent damage to the cover media. In addition to this feature, it is crucial to enhance the data security of hidden information. This paper introduces a methodology that combines Generative Adversarial Networks (GANs) with steganography and cryptography techniques to advance reversible data hiding and enhance data security. By integrating Generative AI -enhanced steganography, the paper aims to embed data within cover images in a reversible manner, ensuring that the original media can be recovered with improved image quality compared to the original image. Furthermore, the integration of cryptography techniques ensures the integrity and confidentiality of the concealed data, making it resistant to unauthorized access and detection. The synergy between Generative AI -driven steganography and cryptography in RDH helps sustain key elements of reversible data hiding, including reversibility, embedding capacity, and data security. Enhancing performance can be applied in various fields such as medical imaging, forensics, intellectual property protection, secure communications, and military applications. © 2025 Elsevier B.V., All rights reserved.