To introduce the fundamentals and applications of Graph Machine Learning (GML), exploring how graph-based data structures can be leveraged to improve learning models. The session aims to provide insights into core concepts such as Graph Neural Networks (GNNs), node and edge embeddings, and real-world use cases in recommendation systems, social networks, and biological data analysis. Attendees will gain a foundational understanding of GML techniques and their relevance in solving complex, structured data problems.