Graph neural networks (GNNs) have emerged as a powerful tool for analyzing graph-structured data, making significant strides in various applications such as social network analysis, recommendation systems, and bioinformatics. Despite their growing popularity and success, GNNs face several challenges that hinder their full potential. This chapter delves into the primary obstacles encountered in the development and deployment of GNNs, including scalability issues, the intricacies of graph data, and the complexity of designing effective GNN architectures. Firstly, the scalability of GNNs poses a significant challenge. As the size of the graph increases, so does the computational burden, leading to inefficiencies and limitations in handling large-scale graphs. Techniques such as graph sampling and parallelization have been proposed to mitigate these issues, yet they often introduce trade-offs between accuracy and computational efficiency. Secondly, the inherent complexity of graph data, characterized by its non-Euclidean nature, demands sophisticated methods for effective representation and processing. Traditional neural networks are ill-suited for such data, necessitating the development of specialized GNN architectures capable of capturing the intricate relationships within graph structures. However, designing these architectures requires a deep understanding of both graph theory and neural network principles, posing a steep learning curve for researchers and practitioners. Additionally, the interpretability of GNN models remains a critical concern. While GNNs can achieve impressive performance, understanding the reasoning behind their predictions is often challenging. This lack of transparency can hinder their adoption in fields where interpretability is crucial, such as healthcare and finance. Moreover, GNNs are susceptible to issues of over-smoothing and over-fitting. Over-smoothing occurs when repeated message passing leads to node features becoming indistinguishable, while over-fitting happens when the model captures noise instead of underlying patterns. Both issues can degrade the performance of GNNs, making it imperative to develop techniques for regularization and model robustness.