Deepfakes, a novel form of artificial media created using synthetic intelligence, particularly generative adversarial networks (GANs), have raised significant ethical and societal concerns due to their potential for misinformation and manipulation. As their sophistication increases, powerful detection techniques become imperative. This paper presents a comparative analysis of various deepfake detection techniques, including traditional image analysis, machine learning algorithms, and deep learning-based methods. We evaluate the performance, scalability, and reliability of these techniques using several benchmark datasets. Our findings demonstrate a hierarchy of effectiveness and performance among the methods, along with insights into their applicability in real-world scenarios.