In image processing, multi-level thresholding is a sophisticated technique used to delineate regions of interest in images by identifying intensity levels that differentiate different structures or objects. Multi-range intensity partitioning captures the complexity and variability of an image. The aim of metaheuristic algorithms is to find threshold values that maximize intra-class differences and minimize inter-class differences. Various approaches and algorithms are reviewed and their advantages, limitations, and challenges are discussed in this paper. In addition, the review identifies future research areas such as handling complex images and inhomogeneous data, determining thresholding levels automatically, and addressing algorithm interpretation. The comprehensive review provides insights for future advancements in multilevel thresholding techniques that can be used by researchers in the field of image processing.