Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold. Firstly, EPO has low learning curve (i.e., based on the simple analogy of huddling behavior of emperor penguins in nature (i.e., surviving strategy during Antarctic winter). Secondly, EPO offers straightforward implementation. In the EPO, the emperor penguins represent the candidate solution, huddle denotes the search space that comprises a two-dimensional L-shape polygon plane, and randomly positioned of the emperor penguins represents the feasible solution. Among all the emperor penguins, the focus is to locate an effective mover representing the global optimal solution. To-date, EPO has slowly gaining considerable momentum owing to its successful adoption in many broad range of optimization problems, that is, from medical data classification, economic load dispatch problem, engineering design problems, face recognition, multilevel thresholding for color image segmentation, high-dimensional biomedical data analysis for microarray cancer classification, automatic feature selection, event recognition and summarization, smart grid system, and traffic management system to name a few. Reflecting on recent progress, this paper thoroughly presents an in-depth study related to the current EPO's adoption in the scientific literature. In addition to highlighting new potential areas for improvements (and omission), the finding of this study can serve as guidelines for researchers and practitioners to improve the current state-of-the-arts and state-of-practices on general adoption of EPO while highlighting its new emerging areas of applications.