Numerous algorithms have been developed for continuous single-objective optimization (SOO), with performance evaluations typically based on statistical analyses over benchmark problems. However, this approach has limitations, as results often fail to generalize to new problem instances, impeding our ability to build trustworthy optimization methods. A significant challenge is the lack of understanding of algorithm behavior, which varies across different optimization problems, treating these algorithms as black boxes. Landscape analysis offers insights into the meta-features of optimization problems, which are crucial for interpreting algorithm behavior. With the rise of Explainable AI (xAI), there is a growing interest in applying xAI to evolutionary algorithms. This chapter reviews our research on explainable landscape analysis and introduces a novel experiment using xAI to identify key landscape features that characterize optimization problems.