Graph Neural Networks (GNNs) are some of the new machine learning technologies that have the ability to process complex graph-structured data. This chapter will outline the different applications that can be made for GNNs across various domains with special emphasis on their usage in real life. GNNs can process non-Euclidean data sophisticatedly owing to its inherent intrinsic complex relationships in that data, and that’s why GNNs are so much more valuable for social network analysis, medical field applications, transportation, and the like. Some of these advantages are better interpretability, scalability for large datasets, and inductive learning of the capabilities over traditional methods. This chapter includes basic concepts and architectures, such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs), to discuss the applications of GNN. Some examples of industry-specific GNN applications include the user recommendation systems, drug discovery, traffic prediction, etc. It will detail emerging trends in cybersecurity and IoT cross-domain applications. Despite the scope for innovation, scalability and interpretability would still be great challenges in GNNs. Future research directions and possible applications which can further exploit the power of GNNs in solving some really complex problems across the sectors will also be sketched in this chapter.