This paper investigates higher-order probability distributions and their applicability to modeling complex stochastic phenomena in various scientific fields. Traditional probability models often fail to capture the intricate dependencies inherent in these phenomena. By leveraging higher-order distributions, researchers can better describe relationships among variables and predict complex systems' behaviors more accurately. We provide an overview of key studies, analyze methods employed, and suggest directions for future research.