The advancement of deep learning and generative artificial intelligence has significantly the positive and negative aspects of multimedia content in social networking communities. Recently, numerous complaints have been filed worldwide regarding deepfake videos. These shared deepfake videos have degraded the trust of individuals and communities. Deepfake video detection is a major challenge due to the high similarity index betweendeepfakeand real videos. Deepfake detection techniques mostly target video media covering manipulations like DeepFakes, Face2Face, and FaceSwap with limited exploration of still images. Approaches that incorporate data augmentation or self-blended image generation tend to improve cross-domain performance, yet several methods experience significant degradation when tested on low-resolution or mixed-manipulation synthetic media. The objective of this paper is to provide the researcher a better understanding of how deepfakes are generated and identified, the latest developments and breakthroughs in this realm, weaknesses of existing security methods, and focus on areas requiring more investigation. Reviewed research paper results mostly based on deepfake detection methods using convolutional neural networks, EfficientNet, and hybrid architectures consistently achieve in‐library accuracies between 90% and 100% on benchmark datasets such as FaceForensics++ and CelebDF. In contrast, cross-library evaluations reveal performance drops: some methods maintain accuracies above 90%, while others decline into a 50% to 90% rang for example, one method reported an in‐library AUC of 97.2% that fell to 57.2% across l ibraries. Only a few studies specify computational costs; one reports inference times exceeding 5 ms per frame on an NVIDIA 2080Ti GPU with 128 GB RAM.