The rapid spread of deepfake videos poses significant challenges to the credibility of digital media, raising concerns over pri- vacy, misinformation, and trustworthiness. This research introduces a hybrid model combining Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to enhance deepfake detection. By leveraging ResNeXt-50 for extracting relevant features and LSTMs for capturing frame-to-frame dependencies, the proposed architecture effectively detects altered facial features in videos. Key preprocessing techniques, including face detection, extraction, and segmentation, optimize input data by isolating relevant facial regions. Experimental results demonstrate that this approach outperforms current methods in identifying subtle deepfake artifacts, underscoring the need for robust detection mechanisms to protect the credibility of digital media. Future work will explore improved scalability and real-time applications of this technique.