Deepfake is a technology for generating fake news or fake media based on images, videos, or audio that look genuine but are fake or synthetic. But this raises a bigger question—how to know that the image, video, or audio is not fraudulent and is not designed to mislead and manipulate people. Deepfakes can have deadly consequences, altering reality and eroding confidence in the media. The study provides an assessment of various deep learning techniques to detect deepfakes. Positive and negative aspects of deep learning are examined: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks, Generative Adversarial Networks (GANs), and transformer models. In this study, the optimal way of detecting deepfakes has been examined, and assessed whether implementing hybrid methods with various detection methods aids in expediting the process. Through this comparative analysis of these techniques, a generous contribution to the field of deepfake detection is made, providing a robust and accurate means of detecting manipulated content.