This study proposes a hybrid approach combining classical and quantum machine learning techniques for breach detection and trajectory monitoring in data link networks. The system integrates environmental variables like terrain and weather into predictive models, enhancing the robustness of defence communication systems. The MIL-STD 1553 protocol is extended with a multi-layered cybersecurity framework for dynamic data link scenarios. Using hybrid models such as support vector regression (SVR), random forest (RF), and quantum neural networks (QNN), the system predicts trajectories and identifies breaches in real time. Experimental results demonstrate that the hybrid model achieves a prediction accuracy of 98.3%, outperforms standalone implementations in scalability and computational efficiency, and reduces breach detection time by 25%. These findings highlight the system’s potential as a comprehensive and reliable framework for aerospace and defence applications, ensuring both operational efficiency and security.