This presentation begins with an overview of graph-based machine learning, illustrating how graphs naturally and flexibly represent data and capture additional network structure signals. These capabilities enhance reasoning, knowledge retrieval, and predictive performance of Machine Learning pipelines. The presentation then explores the synergies between graph learning and generative AI, showcasing how these two approaches complement one another. Amin will demonstrate how integrating graph-based techniques with large language models (LLMs) unlocks powerful new capabilities in AI systems.