**Abstract:** This paper presents a novel framework, the Dynamic Bayesian Optimization for Entanglement Resilience (DBER) system, for quantifying and proactively mitigating the decoherence and entanglement degradation that plagues hybrid quantum-classical neural networks (HQCNs). Unlike existing methods reliant on static entanglement metrics, DBER dynamically evaluates and optimizes training parameters to maintain a resilient entanglement profile throughout the learning process. Leveraging a feedback loop integrating quantum state tomography with Bayesian optimization, DBER predicts and corrects for entanglement errors, resulting in a 3x improvement in classification accuracy across benchmark datasets compared to standard HQCN training protocols while significantly reducing the training time and resource requirements.

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