This chapter provides an overview of the newly developed field of quantum machine learning (QML) and the diversification of its fields of uses. When integrated with the principles of quantum computing, and machine learning, QML provides enhanced computational power, which is likely to change the ways various sectors handle data and solve problems. The chapter also offers an extended discussion of the current applications of QML in finance, healthcare, cybersecurity and artificial intelligence. In applying QML to finance, it has potential in portfolio optimization, fraud detection and market prediction. In healthcare it holds promise in creating new drugs and therapies, in genetic diagnosis, and in medical imaging. The other areas of cybersecurity applications include improved methods of encryption, an anomaly detection system, and blockchain security. The chapter also highlights the application of QML in enhancing the natural language processing and reinforcement learning in artificial intelligence. Alongside with the general and specific benefits of the new field QML, the authors under consideration focus on the existing problems, such as the limitations of the hardware, algorithmic difficulties and integration problems. Finally, further considerations including future trends are reviewed based on the idea that enhancement of quantum hardware, algorithm, and interdisciplinarity would be crucial for the further development of QML in various areas.