Stochastic Dual Dynamic Programming (SDDP) is widely recognized as the predominant methodology for solving large-scale multistage stochastic linear programming (MSLP) problems. This paper aims to contribute to the extant literature by conducting a comprehensive survey of the literature on SDDP within the realm of practical applications. We systematically identify and analyze the various domains where SDDP has been successfully employed to tackle MSP problems, with a particular focus on real-world problems afflicted by the so-called \textit{curse-of-dimensionality}. Furthermore, we investigate the factors that have facilitated or hindered the adoption of SDDP in specific application areas, shedding light on the limitations and potential barriers to its widespread utilization.