Pavement cracks are a common issue affecting road infrastructure worldwide. Their timely detection and repair are essential for ensuring road safety and preventing further deterioration. Traditional manual crack inspection methods are time-consuming, labor-intensive, and susceptible to human mistakes. To overcome these challenges, this thesis introduces CrackGNet, a hybrid deep-learning architecture for the automated detection of road cracks. Our proposed method strategically integrates a U-Net encoder-decoder with a GCN. The U-Net component focuses on robust feature extraction from crack images, while the GCN component explicitly models the spatial relationships and connectivity patterns inherent in crack structures. We performed a comprehensive evaluation of CrackGNet on the CFD, DeepCrack, and CRACK500 datasets, demonstrating its superiority over established deep learning baselines across a range of performance metrics achieving 0.8618 precision, 0.8811 recall, and an F1 score of 0.8665, outperforming existing methods. These results highlight the effectiveness of our hybrid approach and the importance of considering both local image features and spatial context in crack detection tasks. Our research contributes to the advancement of automated road infrastructure assessment systems, with the potential to improve infrastructure maintenance efficiency and reduce costs. Future research directions include exploring alternative GNN architectures for further performance gains, expanding datasets for increased model robustness, and evaluating CrackGNet in real-world inspection scenarios.