With the rapid advancement and accessibility of deepfake technology, the creation of highly convincing yet fraudulent digital content—such as images, videos, and audio—has emerged as a significant threat to cybersecurity, privacy, and public trust. Deepfakes are increasingly used to spread misinformation, manipulate media, and commit fraud, highlighting the urgent need for advanced detection methods to counter these risks. This paper presents Vision Transformers (ViT) as a powerful and robust solution for detecting deepfake content by leveraging state-of-the-art AI and machine learning techniques. Unlike traditional Convolutional Neural Networks (CNNs), which struggle with high-dimensional data, the ViT model processes images as sequences of patches, allowing it to detect subtle inconsistencies such as unnatural facial movements, lighting anomalies, and texture irregularities that are characteristic of deepfakes. The system is trained on a comprehensive 2 GB dataset containing both real and fake media, using data augmentation and enhanced training techniques to improve the model's generalization and robustness. After 300 training epochs, the ViT model achieved a high detection accuracy of 94.85%, significantly outperforming traditional CNN-based methods. To further ensure reliability, the system integrates post-processing steps such as metadata analysis and digital signature verification, making it highly effective for real-time applications in digital forensics, cybersecurity, and fraud prevention. This paper also examines the broader societal and legal implications of deepfake technology, emphasizing the growing importance of developing reliable tools to mitigate misuse. As deepfakes continue to evolve, proactive solutions like ViT-based detection systems are essential for safeguarding the integrity of digital content in an increasingly interconnected world.