Quantum Machine Learning (QML) attempts to address the computational difficulties in Artificial General Intelligence by combining the principles of machine learning and quantum-based computing. QML applies quantum effects, like entanglement and superposition, to enhance the handling of high-dimensional complex data tasks. This chapter discusses the theoretical basis of QML: quantum states, gates, and algorithms, including Quantum PCA and amplitude amplification. It addresses the pairing of machine learning models and quantum computing, as well as potential advantages over conventional techniques like quicker, more precise, and scalable computing. For QML, applications in robotics, optimization, and natural language processing are discussed, which presents AGI as potentially revolutionized. Technical challenges coming from noise and data representation are considered with solutions to overcome them. Emerging trends such as quantum-enhanced NLP and hybrid quantum–classical models are discussed to envision QML’s role in advancing AGI. The future prospects of QML are analyzed by focusing on its ability to revolutionize intelligence systems. This chapter delves further into and incorporates QML into adaptive systems, demonstrating how QML can be possible in real time, as well as through decision-making and learning paradigms. Relating the progress in quantum computing hardware with the advancement of QML algorithms, the examples illustrate how advances in either field drive each other. Such types of problems that are usually considered difficult to solve, such as protein folding and large-scale climate simulations, have now become susceptible to QML. Finally, ethical implications and governance issues with respect to their application into AGI systems are also addressed to secure their responsible use in determining the future of intelligence.