In the field of Fuzzy Mathematics, Computational Logic and Applied Analysis, chaotic fluctuations in non-linear memristive neural networks cause signal desynchronization in secure communications. This empirical investigation systematically examines Adaptive Sliding Mode Synchronization of Fractional-Order Chaotic Systems in Fuzzy Memristive Circuits through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing fractional-order sliding mode control with adaptive fuzzy parameter tuning and Lyapunov asymptotic stability verification, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the controller achieved master-slave chaotic synchronization in sub-second duration despite 30% external noise disturbances. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for secure communications engineers and hardware neuromorphic circuit designers. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.