Graph learning is an emerging field in artificial intelligence that leverages Graph Neural Networks (GNNs) and other advanced techniques to analyze complex interconnected systems. This approach excels at decoding intricate relationships within data structures that conventional methods often find challenging. Graph learning has shown promise in various scientific domains, particularly in biology and chemistry. In drug discovery, these models aid in identifying potential new medicines by predicting molecular properties. Biologists use graph learning to study protein structures and gene networks, potentially advancing our understanding of complex diseases. Materials scientists explore the field to design materials with specific properties, with potential applications in energy storage and engineering. Beyond science, graph learning finds use in social network analysis, recommender systems, and logistics optimization.