The surge in digital media and the rapid evolution of deep learning have paved the way for deepfake technology, posing serious threats to the credibility of information and public trust in digital content. Deepfakes have quickly become a pressing concern in today’s tech-driven world, blending artificial content with real footage so seamlessly that it’s nearly impossible to distinguish one from the other with the naked eye (Maryam, Hongmei, Comparison of Deepfake Detection Techniques through Deep Learning). This technology has disrupted various industries and sparked intense debates around AI ethics and its potential for misuse. From the artificial modification of public figures to their impersonation, deepfakes are utilized on an unimaginable level to inflict damage, bring havoc, and disseminate misleading information globally with utmost ease. Combating this problem requires a lot of approaches at the same time. Detection methods that combine Convolutional Neural Networks (CNNs) to catch fine visual discrepancies, along with Recurrent Neural Networks (RNNs) that pick up on inconsistencies over time, have shown considerable promise (Li, Lyu, DeepFake Videos By Detecting FaceWarping Artifacts). These systems use the same deep learning principles behind the creation of deepfakes to expose them—analyzing subtle cues and hidden patterns that often go unnoticed by the human eye. As the technology is bound to evolve and become more advance, the methods will adapt too. Still, there are significant challenges, especially when it comes to keeping pace with evolving manipulation techniques and ensuring models remain robust against new forms of tampering. Beyond just technical solutions, spreading awareness about the impact and risks of deepfakes is equally important. Restoring confidence in digital media depends not only on smarter detection tools but also on collective efforts to inform and equip people to handle this new digital landscape with caution and responsibility.