This study introduces an innovative approach to enhance surface plasmon resonance (SPR) sensors by integrating photonic crystal structures with deep learning-based optimization. Addressing the research gap in comprehensive frameworks that simultaneously optimize geometric and material parameters, the methodology consists of defining structural parameters, generating a training dataset via FDTD electromagnetic simulations, training a deep autoencoder model, and implementing a multi-stage optimization process combining random search, gradient-based SLSQP, and genetic algorithms to achieve globally optimal sensor designs. Experimental results demonstrate that the optimized photonic crystal SPR sensor achieves a high sensitivity of 99.22 nm/RIU and excellent linearity (R² = 0.9998). A low limit of detection (LOD) of 245.92 ng/mL, alongside binding kinetics analysis, further confirms the sensor's capability for real-time monitoring of molecular interactions. The main innovation lies in the synergistic use of deep learning and photonic crystal engineering, enabling simultaneous optimization of multiple design parameters and significant performance enhancement. This sensor combines nanophotonic engineering and artificial intelligence to enable the development of defense systems with rapid detection capabilities, high precision, and optimized energy consumption.