<p>Quantum computing has become popular as a result of the rise in computational demands for solving issues in a variety of sectors that are computationally expensive to tackle with conventional computers. Quantum computers are based on ideas taken from quantum mechanics including superposition, entanglement, tunnelling, and annealing. Numerous industries, including artificial intelligence, cryptography, and communication, have found use for quantum computing. A novel family of quantum algorithms has emerged to challenge the conventional wisdom on the limited applications of a quantum computer. In addition to providing exponential speedups over current classical techniques, these new algorithms also do so for a number of eminently realistic problems, including machine learning problems like clustering of data, classification of data into different classes, and finding patterns in massive number of available data. At the nexus of two of the most intriguing current fields of research, conventional machine learning and quantum computing, lies quantum machine learning. Quantum machine learning (QML) is an emerging field that merges the principles of quantum computing with machine learning, aiming to leverage quantum computational power for complex data processing tasks. This paper provides a systematic categorization of QML, structured around three fundamental dimensions: learning paradigms, noisy intermediate-scale quantum (NISQ) devices, and fault-tolerant design. Initially, we classify QML techniques into supervised, unsupervised, and reinforcement learning paradigms, elucidating how quantum approaches can enhance traditional machine learning tasks. Subsequently, we examine the capabilities and constraints of NISQ devices, which represent the current generation of quantum hardware, and their impact on the development and performance of QML algorithms. Finally, we delve into fault-tolerant quantum computing, discussing the importance of error correction and robust algorithmic frameworks necessary for achieving reliable and scalable quantum machine learning. By categorizing QML along these lines, we aim to provide a clear and comprehensive overview of the field, highlighting the current state of research, practical implementations, and future challenges in advancing quantum-enhanced machine learning.</p>