The spread of high-fidelity DeepFake content has created extremely serious problems in the politics, society, and security domains. Conventional detection techniques, which rely mainly on convolutional neural networks (CNNs), are increasingly being replaced with more advanced synthetic generation mechanisms. With their natural self-attention and ability to model global context, Vision Transformers (ViTs) have proven to be promising substitutes. This review comprehensively examines 160 contributions spanning diverse approaches in deepfake detection, from temporal inconsistency exploitation to hybrid CNN–ViT architectures and fairness‐aware methods. We analyze the evolution from conventional CNN-based forensic detectors to the state-of-the-art ViT approaches, highlight technical innovations in patch embeddings and multi-head attention, and compare performances on benchmark datasets. Open issues—such as data imbalance, computational overhead, robustness against adversarial techniques, and ethical fairness—are discussed. Finally, future research directions are outlined, including self-supervised learning, efficient attention approximations, continual adaptation strategies, and multimodal extensions. This review demonstrates that while ViTs have significantly advanced deepfake detection, further interdisciplinary research is needed for robust, real‐world deployment [1–159].