<p>With the fast growth of digital communication, it has become important to share information securely and secretly, especially through images and audio. Normal encryption methods can hide the content, but not the act of sending secret data. This is where steganography helps. It hides the secret message inside normal-looking files. However, many existing methods have problems like low capacity for hiding data, weak protection against detection, and poor performance with different media types. This paper gives a detailed review of the latest steganography techniques used for image and audio files. It explains how these methods work, their benefits and limitations, and where they can be used. In recent years, steganography has progressed significantly with the introduction of intelligent and adaptive techniques. Convolutional neural networks (CNNs), generative adversarial networks (GANs), and deep learning models are being used more and more in modern techniques to increase embedding efficiency and resistance to advanced steganalysis. Hybrid encryption steganography frameworks and content-aware embedding strategies have also enhanced payload capacity, imperceptibility, and robustness across different multimedia formats. We also include standard datasets like BOSSbase and ALASKA for image steganography, and ESC-50 for audio, to fairly compare different methods. Finally, the paper discusses current research challenges and emerging future directions to further enhance the security, adaptability, and intelligence of modern steganographic systems.</p>