This work pioneers a universal framework for quantum entanglement quantification in nuclear systems through innovative integration of quantum distance metrics and machine learning. We introduce a framework for quantifying entanglement in nuclear spin systems using quantum distance measures (fidelity, trace distance, Bures distance (DB) ). A universal scaling law DB ∝ Γ−0.85 links Bures distance to nuclear decay width, experimentally validated in 56Fe(n, γ) reactions (DB = 0.62±0.04). Machine learning achieves 94% prediction accuracy, revealing 18% entanglement enhancement near shell closures. Three entanglement phases guide quantum technology applications: 56Fe for memory (DB > 0.6, τD ∼ 10−25s) and 6Li for sensing. The study bridges quantum information theory and nuclear physics, offering transformative tools for both fields.
