Multiscale Entropy (MSE) is a nonlinear measure used to quantify the complexity of time series and has been widely applied to reveal the multiscale regulation mechanisms in postural control. Unlike traditional linear metrics that focus on single-scale statistical properties, MSE captures cross-scale temporal dynamics and provides a more sensitive indicator of physiological integrity and functional decline. This review summarizes recent progress in the use of MSE in postural control studies, including fall risk prediction, disease-specific mechanism identification such as Parkinson’s disease, multiple sclerosis, and schizophrenia, and intervention assessment involving practices like Tai Chi and neuromodulation. MSE demonstrates sensitivity to both behavioral output and central regulation. However, the lack of standardized settings for parameters such as embedding dimension, tolerance, and scale factor limits comparability across studies. Future work should unify key parameter ranges, such as setting embedding dimension to 2 and tolerance to 0.15–0.25 times the signal standard deviation, and develop adaptive algorithms that adjust scale factors according to task demands like static standing or gait. MSE offers an integrated framework for analyzing postural control mechanisms across behavioral and neural domains and holds potential as a tool for complexity-based rehabilitation assessment and intervention optimization.