Atmospheric light (AL) refers to the ambient illumination present in Earth's atmosphere resulting from the scattering, absorption, and reflection of sunlight by air molecules, aerosols, and particulate matter. This light contributes to the overall brightness, coloration, and visibility of the sky and surrounding environment. AL is a key factor in determining how the atmosphere appears at various intervals of the day, influencing things like the color of the sky, the tints of sunrise and sunset, and the visibility of far-off objects. It is a fundamental component in various scientific disciplines, including meteorology, astronomy, image dehazing and environmental science, and is essential for understanding atmospheric dynamics and its impact on human experiences and natural processes. AL plays a momentous role in the process of dehazing, which is the task of recovering clear and visually appealing images from hazy or foggy scenes. Understanding the impact of AL in this context is crucial for developing effective dehazing algorithms and techniques. Traditional methods such as Brightest Pixel Selection (BPS), Dark Channel Prior (DCP), Median Filtering, Gradient-based methods, Color Constancy-based Methods, and Regression based methods are employed to estimate AL to boost the efficacy of Image Dehazing. Though Traditional strategies for estimating AL have been widely used and studied, they may have limitations in terms of robustness, adaptability and accuracy, especially in challenging or dynamic environments. This has spurred the development of more advanced approaches which aim to address these limitations and achieve more accurate and reliable estimates of atmospheric light.