In the field of Engineering, Mechanical Innovation and Advanced Technology, stochastic intermittent generation from distributed wind and solar arrays destabilizes islanded microgrid frequency and voltage. This empirical investigation systematically examines Hybrid Wind-Solar Microgrid Dispatch Optimization Using Grey Wolf Optimization and Deep Neural Networks through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing hybrid grey wolf optimization (GWO) coupled with LSTM neural network multi-step renewable generation forecasting, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the dispatch controller reduced fuel operating costs by 19% and stabilized microgrid frequency fluctuations within +/- 0.05 Hz. 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 renewable microgrid operators and decentralized rural electrification projects. 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.