The rapid growth of Edge-Industrial Internet of Things (Edge-IIoT) systems highlights the need for efficient and accurate intrusion detection systems (IDS) that can address the complexity and scale of modern cyberthreats. Traditional IDS solutions, though effective in limited scenarios, often struggle in dynamic and heterogeneous Edge-IIoT environments. To overcome these limitations, we present QCShield, a quantum-classical hybrid framework that leverages Quantum Approximate Optimization Algorithm (QAOA) embeddings and advanced classical neural network architectures. QCShield integrates sophisticated quantum layers with convolutional neural networks (CNNs) for robust feature extraction and bidirectional long short-term memory (BiLSTM) networks for effective sequence processing. The quantum component efficiently captures intricate feature interactions and uncovers subtle anomalies in high-dimensional data, while the classical component ensures accurate classification of threats. This hybrid architecture delivers a significant speedup in intrusion detection while maintaining high accuracy and scalability. Experimental results demonstrate that QCShield provides a notable performance boost over traditional IDS methods, offering enhanced detection capabilities in challenging Edge-IIoT environments. This hybrid model paves the way for more efficient and effective security solutions in Edge-IIoT systems.