Deepfake technology has reached unprecedented scales and has reached critical mass among society. Deepfake technologies enable the making of virtually ‘real’ but fully fabricated audio-visual media, thus leaving significant indelible footprints with the public (the public). Many advanced artificial intelligence techniques like deep learning are used in this technology to create synthetic material, which is capable of performing believable simulations of real people in video or audio content, much without their knowledge or consent. Deepfake devices represent an effective means of disinformation for digital security e. g. human rights, political discourse, information integrity, and trust. The result of recent developments in the field of deepfake detection relies predominantly on machine learning models that generalize efficiently and reliably, particularly the most effective models, along the theoretical semantic space constraints, by using both spatial and temporal features (for example, convolutional neural networks can be used for generalizing representations of information), and obtain reliable detection results. This paper presents an overview of the current technologies that are used for the detecting deepfake materials. It provides specific contributions on the design of the architecture for these systems, and their relative effectiveness in various salient problems, like generalization accuracy, robustness, and real-time deployment. In addition, we look at the standard datasets for training and testing of deepfake detection systems, highlighting their scope and limitations, and relevance to real-world applications. The paper objectives include providing a thorough review of recent progress on the issue, as well as warning signs of critical gaps in current approaches, and discussing possible future directions. These efforts seek to mitigate various threats of deepfake technologies and to promote the development of digital content authentication systems.