Steganography is the method of hiding information within another medium, usually images. The method of steganography has been documented since ancient Greek times. With advancements in computer science, we can find new and improved methods for image steganography. This study proposed a method to utilize CNN to perform image steganography, the proposed CNN's architecture consists of sequential phases and layers. There are three main phases, the preparation phase, the hiding phase, and the output phase respectively. The hiding phase is responsible for mapping the high-dimensional input image to the lower-dimensional prep image, the hiding phase is the main phase which hides the image and output phase reveals the processed and the hidden image. The CNN is trained on a large and diverse dataset, and with the help of optimization provided by mean squared error between input and output images. The effectiveness of CNN has been measured in this paper with the help of SSIM and FSIM scores. The CNN’s versatility and robustness highlight its potential applications in image steganography.