Deepfake technology, accelerated by advancements in deep learning, poses significant threats to digital media integrity. This study evaluates four key models: CNN, ResNet, Logistic Regression, and Vision Transformers (ViT) for their ability to detect deepfakes by analyzing spatial features, fine-grained anomalies, and classification efficiency. This research compares the models based on accuracy, computational efficiency, and robustness, using datasets from FaceForensics++. The results highlight the superior accuracy of deep learning models but emphasize trade-offs in computational cost for real-time applications in areas such as social media monitoring and cybersecurity. This work advances digital forensics, aiming to combat misinformation and to uphold trust in AI-driven content.