Over the past few years, the proliferation of deepfake content—digitally manipulated media that successfully alters appearances or voices—has created considerable challenges in social, ethical, and cybersecurity realms. Existing deepfake detection approaches, based mostly on Convolutional Neural Networks (CNNs), tend to have difficulty with capturing fine details of subtle facial irregularities and spatial relationships. To overcome this, we introduce a hybrid model that incorporates Graph Convolutional Networks (GCNs) and CNNs. Our model utilizes GCNs to study facial landmarks as graphs, preserving relational information, while CNNs concentrate on pixel-level features in images. By combining results from both models, we provide a strong methodology that takes advantage of the respective strengths. We test our approach on a deepfake dataset taken from kaggle, demonstrating better accuracy compared to conventional CNN-based methods, especially in detecting subtle manipulations. This work provides a novel hybrid system to improve the accuracy of deepfake detection.