With the scale of AI systems growing and datasets growing more complex, legacy machine learning methods start to break down when matched with speed, scalability, and resource consumption challenges. Quantum Machine Learning (QML) is a novel way of looking at things, which looks to integrate the pattern recognition capabilities of machine learning and the probabilistic potential of quantum computing. Things like superposition, entanglement, quantum interference have the potential to change the way we do things in circumstances in which classical models really suck such as optimization, search, and data classification. This chapter is heavy on the theory behind QML and how it fits into the real world. It covers the most pivotal concepts in quantum computing, the most influential machine learning models and why the two domains ought to work together. There are various other QML algorithms referred to in the literature such as Quantum Support Vector Machines (QSVMS), Quantum Neural Networks (QNN), and Variational Quantum Classifiers (VQC). It also addresses how to encode quantum data and systems that are quantum and classical. First, we will learn how these concepts are applied to the real world in environments such as healthcare, finance, and materials research. Then, we conduct a comprehensive case study to bridge the gap between theory and practice. The chapter concludes with a discussion and ethics and technology and with pointers for future research.