The proliferation of Deepfake technology has raised concerns about its potential misuse for malicious purposes, such as defaming celebrities or causing political unrest. While existing methods have reported high accuracy in detecting Deepfakes, challenges remain in adapting to the rapidly evolving Deepfake technology and developing efficient and effective detectors. In this paper, we propose a novel approach to address these challenges by utilizing advanced Neural Architecture Search (NAS) methods, specifically focusing on DARTS, PC-DARTS, and DU-DARTS. Our experimental results demonstrate that the PC-DARTS method achieves the highest test AUC of 0.88 among the techniques investigated, with a learning time of only 2.86 GPU days. This highlights the efficiency and effectiveness of our approach in automatically building Deepfake detection models. Moreover, the models using NAS exhibit competitive performance compared to state-of-the-art architectures such as XceptionNet, EfficientNet, and MobileNet. Our results suggest that the automatic search process using advanced NAS methods can quickly and easily construct adaptive and high-performance Deepfake detection models, indicating a new and promising direction for combating the ever-evolving Deepfake technology.