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Description: Advancements in the field of deep learning have revolutionized various domains, demonstrating significant success in solving complex problems, ranging from big data analytics to computer vision and human-level control. The evolution of deep learning algorithms, particularly in the context of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), has played a pivotal role in pushing the boundaries of artificial intelligence. Deep learning, with its ability to automatically learn intricate patterns and representations from vast amounts of data, has found application in the creation of deepfake technology. Deepfakes are synthetic media, including images and videos, generated through sophisticated algorithms that utilize deep learning techniques. These algorithms enable the creation of hyper-realistic forgeries that can be indistinguishable from authentic content, posing serious threats to privacy, democracy, and national security. This paper conducts a thorough examination of the creation and detection technologies associated with deepfakes, employing deep learning approaches. We delve into an extensive analysis of various techniques and their application in the detection of deepfakes. This comprehensive study is poised to benefit researchers in the field by covering state-of-the-art methods for identifying deepfake videos or images within social contexts. Furthermore, our work facilitates meaningful comparisons with existing research, providing detailed descriptions of the latest methods and datasets utilized in this rapidly evolving domain. Through this contribution, we aim to aid the development of new and more robust methods to address the growing challenges posed by deepfake technologies. Keywords—Deep learning, Deepfake technology, Generative Adversarial Networks (GANs), Synthetic media, Detection technologies, Privacy