The fast growth of deepfake technologies poses a huge dilemma on digital content legitimacy, hence threatening public trust, privacy, and security. This paper introduces a hybrid deepfake detection model that utilizes EfficientNetB0, a lightweight convolutional neural network, with Gated Recurrent Unit (GRU) layers to leverage both spatial and temporal features of videos. The EfficientNetB0-based feature extraction combined with GRU-based sequential learning helps the model to handle inconsistencies between the successive frames in a video. The proposed model utilizes multi-scale feature extraction with transfer learning to capture the complex temporal and spatial patterns suggestive of video manipulation. Extensive experiments conducted demonstrating this as the state of the art were carried out on benchmark datasets, Celeb-DF and DFDC had an accuracy of 89.71%, precision of 88.18%, recall of 82.11%, F1-score of 85.04%, AUC-ROC of 94.56%. These results indicate the robustness of this model and the potential for real-time applications, balancing computational efficiency and detection accuracy, outperforming existing conventional techniques.