Steganalysis, the technique of identifying hidden information in digital media, is becoming increasingly important in security and forensic applications. This article describes a way for expanding steganalysis approaches by incorporating optimized feature extraction techniques to increase detection rates. This article explains various feature extraction techniques and classifiers to improve the distinction between stego and cover media. It focus on the overview of the current state of steganalysis and highlight the limitations of existing techniques for better detection. This study contributes to the ongoing growth of steganalysis approaches by identifying promising pathways for improving security measures and forensic investigations in digital environments. In field of biological sciences, where they can help safeguard sensitive biological data from cyberattacks. Ensuring the security of this data is crucial for the advancement of biological sciences, as well as for preserving the integrity of scientific research and protecting privacy.