In today’s age, data security and privacy are paramount concerns. Reversible data hiding is a technique that’s used to embed data into a cover media in a manner that allows both embedded data and 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. Along with this feature it is necessary for us to increase 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 these cover images in a reversible manner, ensuring that the original media can be recovered with enhancement in image quality in comparison with the original image. Furthermore, the integration of cryptography techniques ensures the integrity and confidentiality of the concealed data. This makes the data resistant to unauthorized access and detection. The synergy between Generative AI-driven steganography and cryptography in RDH helps to sustain key elements of reversible data hiding that includes reversibility, embedding capacity and data security. The enhancement in the performance can be used in various applications such as medical imaging, forensics, intellectual property protection, secure communications, and military applications.