In recent years, advances in technology, specifically in machine learning, artificial intelligence, and deep learning, have made it possible to create highly convincing fake videos known as Deepfakes. These videos are produced by training computer models on extensive datasets of faces and then seamlessly blending one person’s facial expressions onto another’s, resulting in videos that are nearly indistinguishable from reality. The widespread use of Deepfakes presents several concerns, including the creation of political distress, the occurrence of fake terrorism events, the undermining of trust in digital media, etc. Therefore, there is an urgent need to continually advance Deepfake detection and prevention methodologies to safeguard against their malevolent use and maintain the integrity of digital content. This paper conducts a meticulous analysis of the current landscape of Deepfake research, surveys the most effective detection solutions, and introduces a real-time Deepfake detection and prevention model within a rigorous testing framework. This model integrates innovative Blockchain and Steganalysis technologies to provide a robust solution to combat the explosion of Deepfakes. Our holistic framework offers a systematic and statistically rigorous approach to distinguishing genuine content from its manipulated counterparts, Deepfakes. By employing the principles of hypothesis testing, and a robust test statistic, our research equips us with the analytical tools necessary to make well-informed and precise classifications, significantly contributing to the ongoing battle against Deepfakes.