Analysis of a strong turbulence effect on electromagnetic wave propagation requires extensive knowledge of the earth's atmospheric phenomena and Maxwell's electromagnetic theory of light propagation. Strong atmospheric Turbulence often encounters optical systems' propagation, causing interference and diffraction. Nonetheless, light propagation through randomly fluctuating media has been an exciting and active area of extensive research over many decades. This research holistically examined Maxwell's electromagnetic theory of light propagation through unstable medium and its effects (diffraction/interference) on optical systems. Generally, atmospheric Turbulence causes scintillations, beam spreading, phase fluctuations, beam-wander, and jitter that can degrade and limit the performance of imaging and laser systems. However, effectively characterizing strong turbulence effects on electromagnetic wave propagation still needs to be solved. Additionally, the DE, the U.S. Air Force, and the U.S. Space Force must consider strong and substantial turbulence impacts on optical systems. Therefore, we took a holistic approach to examine the problem and revisit existing mitigation measures for gaps. We discussed the refractive index fluctuation (scintillation), a stimulant of atmospheric Turbulence for optical systems and light propagation. Similarly, we investigated Refractive Index Structure Function, Dn(r), Power Spectral Density (PSD), and associated atmospheric parameters. Additionally, we examined multi-concept leading to the Refractive Index Structure Function, Dn(r), and different mathematical formulations of Power Spectral Density (PSD). We formulated an analytical approach to a solution of the wave equation by the multiple integrals (involving complex integrands) technique. We investigated and implemented various mathematical and computational algorithms governing strong atmospheric Turbulence and spatial-temporal imaging. We proposed developing a mathematical framework to simulate optical propagation through Turbulence. We identified helpful software for image processing and an Artificial Neural Network to predict phase screen generation via a Large Language Model.