Deepfake detection is a critical area in digital media, given the rapid advancement and accessibility of deepfake technology. This research paper introduces a sophisticated AI-powered deepfake detection system that leverages the comprehensive FaceForensics++ dataset. Advanced machine learning algorithms including FaceNet for landmark detection, ResNet-50 for robust feature extraction, and GANs for synthetic face generation and detection are used in the system. The presentation of the exemplary is calculated using rigorous metrics, achieving an accuracy of 94.25%, precision of 93.33%, recall of 94.26%, and F1-score of 94.24%, with a mean squared error (MSE) of 0.1775, demonstrating substantial efficacy in identifying manipulated content, and an AUC of 0.99, indicating exceptional accuracy in distinguishing genuine and manipulated videos. To stay ahead of growing deepfake methods, the model will be improved to generalize to unseen alterations and detect in real time.