Quantum Convolutional Neural Networks (QCNNs) have emerged
as one of the most promising architectures in Quantum Machine
Learning (QML), enabling hierarchical quantum feature extraction
and offering potential advantages over classical CNNs in expressivity
and scalability. This study presents a Systematic Literature Review
(SLR) on QCNN development from 2019 to 2025, covering
theoretical foundations, model architectures, noise resilience,
benchmark performance, and applications in materials informatics,
chemistry, image recognition, quantum phase classification, and
cybersecurity. The SLR followed PRISMA guidelines, screening 214
publications and selecting 47 primary studies. The review finds that
QCNNs consistently outperform classical baselines in small-data and
high-dimensional regimes due to quantum feature maps and
entanglement-driven locality. Significant limitations include noise
sensitivity, limited qubit availability, and a lack of standardized
datasets for benchmarking. The novelty of this work lies in providing
the first comprehensive synthesis of QCNN research across theory,
simulations, and real-hardware deployment, offering a roadmap for
research gaps and future directions. The findings confirm that
QCNNs are strong candidates for NISQ-era applications, especially
in physics-informed learning.