Graph Neural Networks (GNNs) have emerged as transformative tools for advancing scene understanding in computer vision, offering powerful mechanisms to capture complex relationships within structured and spatiotemporal data. This paper provides a comprehensive analysis of recent advancements in GNN architectures and their applications in dynamic scene understanding, covering domains such as autonomous driving, video surveillance, and human-object interaction. By leveraging the relational representation capabilities of GNNs, researchers have developed specialized models like spatiotemporal GNNs (ST-GNNs) and hybrid frameworks that integrate GNNs with self-supervised learning, deep reinforcement learning, and attention mechanisms, resulting in state-of-the-art performance on challenging tasks such as early action recognition, few-shot prediction, and scene synthesis. Further, this paper addresses key challenges and ethical concerns associated with deploying GNNs for real-world applications, such as privacy, fairness, robustness, and environmental sustainability. We discuss the ongoing efforts to build trustworthy and scalable GNN models that prioritize transparency, governance, and data protection. Our analysis highlights the necessity of robust frameworks to balance the technological benefits with ethical safeguards, ensuring that GNNs contribute positively to computer vision applications. In closing, we explore promising directions for future research, emphasizing the importance of real-time capabilities, privacy-preserving mechanisms, and fair learning methods. By synthesizing recent innovations and addressing open questions, this paper underscores the potential of GNNs to revolutionize computer vision, while also advocating for a responsible approach to their continued development and deployment.