Technology has advanced rapidly in recent years, resulting in the widespread use of multimedia for data transfer, particularly the Internet of Things (IoT). Typically, insecure network channels were used for transfer. Particularly, the usage of the internet for the exchange of digital media has grown rapidly, and now governments, private businesses, institutions, and individuals all employ this type of multimedia data transfer. Although there are many benefits associated with it, the privacy and security of the data are notable drawbacks. The likelihood of hostile attacks, eavesdropping, and other subversive operations has increased due to the availability of several freely available technologies capable of exploiting the privacy, data integrity, and security of the data transferred. This paper extensively explores diverse classification algorithms with the primary objective of comparative Assessment of Classification Algorithms for A Tuned Machine Learning Model for Steganalysis with effective Feature Extraction Technique. The algorithms under scrutiny cover a wide spectrum, including Ada Boost, Ensemble Classifiers, Naive Bayes, Generalized Discriminant Analysis (GDA), AlexNet-based Single Model Averages (AlexSMA), and Transfer Learning with AlexNet Single Model Averages (T-AlexSMA). The investigation rigorously evaluates their accuracy within the framework of steganalysis. Through comparing their performance, this research aims to uncover the distinctive merits and drawbacks of each algorithm, offering valuable insights for practitioners and researchers within the respective field.These results have applications in the biological sciences, especially in protecting sensitive biological data from cyberattacks. Maintaining the security of biological data is important for the growth of the biological sciences as well as for maintaining the integrity of scientific research and privacy protection.