Quantum Machine Learning (QML) is an emerging research field at the intersection of quantum computing and artificial intelligence, exploring how quantum algorithms can enhance machine learning tasks and how machine learning can aid quantum systems. Foundational research investigates quantum versions of classical models, including quantum neural networks, quantum support vector machines, and quantum Boltzmann machines, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to achieve potential speedups in computation and optimization. Recent studies focus on variational quantum algorithms, hybrid quantum-classical models, and quantum-enhanced kernel methods for applications in classification, regression, clustering, and generative modeling. QML research also addresses challenges in encoding classical data into quantum states, mitigating noise in near-term quantum devices (NISQ era), and developing quantum-inspired optimization techniques. Applications span drug discovery, finance, material science, combinatorial optimization, and large-scale data analysis, positioning quantum machine learning as a promising paradigm for next-generation intelligent systems.