Deepfake video detection is a critical issue due to the increasing use of artificial intelligence (AI) techniques, particularly deep learning, to create convincing fake content. These videos can be used to spread false information, posing threats to politics, security, and privacy. Most deepfake detection methods are data-driven, but they face several challenges, including unbalanced datasets, insufficient labeled training data, and the need for significant computational resources. Detection methods also suffer from overconfidence and the emergence of new manipulation techniques. Despite the dominance of deep learning-based methods in detecting deepfakes, these approaches have limitations in computational efficiency and generalization. The research emphasizes the need for high-quality datasets to improve detection methods and highlights major research gaps, such as the development of robust models for real-time detection. Deepfake media can be of different types based on the content manipulated, including visual, audio, and textual modifications. Visual deepfakes are the most common, involving fake images and videos. These are widely used on social media to spread false information. Face swapping is a common method for creating deepfake images. The paper discusses the evolution of deepfake fraud over the last five years, highlighting key trends and the growing concern around deepfake technology. The study also notes that deepfake media is among the top five identity fraud types in 2023, with a significant increase in the number of video and audio deepfakes. The research aims to thoroughly analyze deepfake video generation and detection, emphasizing the challenges and opportunities in this field.Deepfake video detection is a critical issue due to the increasing use of artificial intelligence (AI) techniques, particularly deep learning, to create convincing fake content. These videos can be used to spread false information, posing threats to politics, security, and privacy. Most deepfake detection methods are data-driven, but they face several challenges, including unbalanced datasets, insufficient labeled training data, and the need for significant computational resources. Detection methods also suffer from overconfidence and the emergence of new manipulation techniques. Despite the dominance of deep learning-based methods in detecting deepfakes, these approaches have limitations in computational efficiency and generalization. The research emphasizes the need for high-quality datasets to improve detection methods and highlights major research gaps, such as the development of robust models for real-time detection. Deepfake media can be of different types based on the content manipulated, including visual, audio, and textual modifications. Visual deepfakes are the most common, involving fake images and videos. These are widely used on social media to spread false information. Face swapping is a common method for creating deepfake images. The paper discusses the evolution of deepfake fraud over the last five years, highlighting key trends and the growing concern around deepfake technology. The study also notes that deepfake media is among the top five identity fraud types in 2023, with a significant increase in the number of video and audio deepfakes. The research aims to thoroughly analyze deepfake video generation and detection, emphasizing the challenges and opportunities in this field.