Digital image steganalysis is a rapidly evolving field that focuses on detecting and analyzing hidden information within digital images, a technique known as image steganography. With the increasing use of digital media and the potential risks associated with covert communication, the development of effective steganalysis techniques has become paramount. This project aims to contribute to the advancement of steganalysis by proposing a novel approach that utilizes convolutional neural networks (CNNs) for the detection of hidden data in digital images. Through the utilization of a carefully curated dataset and extensive research, a robust image classification model is constructed to accurately identify steganographic content. The proposed system analyzes a given image and determines whether it contains any embedded information. By leveraging the power of CNNs, this approach offers a reliable and efficient solution for digital image steganalysis, addressing the challenges posed by modern steganographic methods. The outcomes of this research have the potential to significantly contribute to the field of digital forensics and enhance security measures in the digital realm.