Accommodating the quantum computing within edge and cloud systems reveals a revolutionary way of dealing with the problems of computational and scalability difficulties confronting the classical AI methods. This study examines how Quantum Neural Networks (QNNs) are applied in different areas such as real-time fraud detection, optimization of 5G networks, audio classification, as well as graph data processing, etc., through hybrid quantum–classical models. Experimental assessments show the superiority of quantum-enhanced systems to the traditional architectures in terms of accuracy, computational efficiency, and scalability even if the hardware limitations such as a low number of available qubits and high error rates are present. Hierarchical Quantum Edge Computing (QEC) systems; quantum-enhanced graph transformers; and quantum-driven resource allocation models in 5G point to the benefit of workload distribution across the continuum of the edge-cloud. This is achieved by utilizing quantum-inspired positional encodings and hybrid models, meaning that massive gains are attained in both processing performance and adaptability. On the conclusion of the study, the researchers find that though the quantum AI systems are still showing maturity, they promise an exciting future for smart, high-performance distributed computing. Failure to continue to innovate in hardware, algorithms, and integration strategies will not allow the true potential of quantum computing to be realized in the real world.