Deepfake technology represents a major revolution in synthetic media, enabling highly realistic image and video processing. This chapter explores the core techniques behind deepfake generation, including autoencoders, generative adversarial networks (GANs), and diffusion models, each offering distinct advantages in quality and computational demands. It also examines cutting-edge detection methods, such as forgery analysis, noise detection, and temporal consistency verification, which are critical for identifying synthetic content.The development of deepfake technology hinges on robust datasets, efficient algorithms, and scalable evaluation metrics. By analyzing these components, this work aims to bridge the gap between synthetic media generation and detection, fostering innovation while addressing ethical challenges.