Graph neural networks (GNNs) have become known as a powerful tool in the field of computer vision and creating a framework model for analyzing and processing graph-structured data. The significant application of GNNs in computer vision is incorporated into various models, such as object recognition, image segmentation, video analysis, and 3D reconstruction. GNN represents the real-world data in the form of graphs and visualizes it with more efficiency and accuracy by encoding complex relationships, such as spatial dependencies and semantic interactions. This chapter explores the integration of GNNs into various computer vision tasks and provides an overview of their architectures, such as graph convolutional networks (GCNs), graph attention networks (GATs), and graph recurrent networks (GRNs). Also, discuss the exclusive advantages of GNNs. The paper addresses key challenges that remain in scaling GNNs for large datasets and real-time applications, highlighting ongoing research aimed at improving their computational efficiency. Through this survey, we emphasize the transformative potential of GNNs in computer vision and suggest directions for future research in creating more robust and efficient models for emerging vision-based applications in fields such as healthcare, autonomous systems, and augmented reality.