As Moore’s Law nears the brink of its physical limitations and the demand for computational power surges, quantum machine learning is increasingly expected to be a prospective solution to this pressing issue. In the past decade, quantum computing attempted to build a system analog to classical computing, including fundamental gates, basic algorithms, and the structure of machine learning. This paper initiates with classical computing and transitions into the quantum domain, systematically progressing from foundational theories to the forefront of machine learning applications. The study demonstrates the potentiality of quantum computing within the machine learning arena, while also validating the efficacy of methodologies that emulate classical computing.