The proliferation of deepfake technology has transformed artificial intelligence into a double-edged sword, facilitating innovation while posing significant ethical and security risks. This study reviews the state-of-the-art in deepfake detection, emphasizing the role of deep learning techniques and the utility of emerging datasets. Key datasets such as CIFAKE, COCOFake, and DFFD are analyzed for their contributions to robust model development across diverse manipulation types. Advances in detection algorithms, including capsule networks, attention mechanisms, and binary neural networks, are evaluated for their accuracy, computational efficiency, and generalization across datasets. Furthermore, the review highlights challenges in cross-dataset generalization, real-time detection, and model explainability, proposing future directions for research. This work aims to guide the development of next-generation deepfake detection methods, addressing the dynamic landscape of generative AI.