Quantum Machine Learning (QML) represents the forefront of technology, integrating quantum computation and artificial intelligence to facilitate numerous groundbreaking improvements in artificial general intelligence (AGI).This chapter will explore how the intrinsic qualities of quantum computing—namely parallelism, entanglement, and superposition—can be utilized to enhance machine learning models for addressing complicated issues beyond the capabilities of traditional methods. We introduce fundamental QML techniques, such as quantum data encoding, variational circuits, and quantum neural networks, while highlighting recent advances and challenges. The chapter highlights that the incorporation of QML in AGI frameworks can literally change the way learning, optimization, and decision making are approached, propelling the path to autonomous human-like intelligence in numerous fields ranging from language processing, robotics, to graph-based data analytics.