Fog significantly hinders LiDAR performance in autonomous vehicles due to increased scattering, absorption, and backscattering, which degrade signal integrity and reduce detection range. This review comprehensively analyzes the impact of fog on two prominent LiDAR technologies—Time-of-Flight (ToF) and Frequency-Modulated Continuous Wave (FMCW)—using a Monte Carlo simulation framework. By modeling photon interactions with fog particles, Monte Carlo methods provide critical insights into signal attenuation, range limitations, and backscatter noise. The review also explores the influence of key parameters such as fog density, particle size distribution, and wavelength-dependent scattering on LiDAR performance. Furthermore, it discusses emerging mitigation strategies, including adaptive signal processing, multi-wavelength LiDAR systems, and advanced filtering techniques, to address fog-induced challenges. This survey highlights the versatility of Monte Carlo modeling as a powerful tool for optimizing LiDAR design, enhancing detection accuracy, and ensuring reliable operation in adverse weather conditions. The insights gathered from this review aim to guide future research and our development efforts, fostering innovation in LiDAR technology for safer and more efficient autonomous driving systems.