This review article provides a comprehensive survey of multi-level thresholding segmentation methods 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. The aim of metaheuristic algorithms is to find threshold values that maximize intra-class differences and minimize inter-class differences. The paper reviews various approaches and algorithms, discussing their advantages, limitations, and challenges. It also identifies future research areas such as handling complex images and inhomogeneous data, determining thresholding levels automatically, and addressing algorithm interpretation. The review provides insights for future advancements in multilevel thresholding techniques that can be used by researchers in the field of image processing. Image segmentation involves dividing an image into meaningful, segmentally coherent regions or objects. Thresholding is a simple and direct method of segmenting an image. It allows for the extraction of several sections from an image at once. Image segmentation is popular due to its simplicity and purpose. Multi-level thresholding allows for more flexible and adaptive segmentation because different regions within an image may require different threshold values. Various lighting conditions and complex intensity distributions improve segmentation accuracy. Multi-level thresholding captures finer details and nuances in segmented regions. Metaheuristic algorithms (MAs) are widely used for data processing and solving practical problems like high-dimensional or nonlinear complex computations. MAs have the stochastic search capability to find an optimal solution without traversing the solution space. A large part of threshold segmentation is dependent on MAs. This survey can be used as a valuable resource for developing multilevel thresholding techniques. The proposed survey can aid researchers in identifying areas for improvement and guiding future research. Research on multilevel thresholding techniques in information science can be improved by addressing challenges and limitations identified in this research. The paper is organized into sections that include an overview of existing studies, a literature review, analysis of datasets, open issues, and conclusions. The survey provides a detailed taxonomy of thresholding approaches, thresholding applications and case studies, and a description of existing image segmentation techniques. It also discusses simulation environments, programming languages, case studies, evaluation metrics, and research gaps.This review article provides a comprehensive survey of multi-level thresholding segmentation methods 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. The aim of metaheuristic algorithms is to find threshold values that maximize intra-class differences and minimize inter-class differences. The paper reviews various approaches and algorithms, discussing their advantages, limitations, and challenges. It also identifies future research areas such as handling complex images and inhomogeneous data, determining thresholding levels automatically, and addressing algorithm interpretation. The review provides insights for future advancements in multilevel thresholding techniques that can be used by researchers in the field of image processing. Image segmentation involves dividing an image into meaningful, segmentally coherent regions or objects. Thresholding is a simple and direct method of segmenting an image. It allows for the extraction of several sections from an image at once. Image segmentation is popular due to its simplicity and purpose. Multi-level thresholding allows for more flexible and adaptive segmentation because different regions within an image may require different threshold values. Various lighting conditions and complex intensity distributions improve segmentation accuracy. Multi-level thresholding captures finer details and nuances in segmented regions. Metaheuristic algorithms (MAs) are widely used for data processing and solving practical problems like high-dimensional or nonlinear complex computations. MAs have the stochastic search capability to find an optimal solution without traversing the solution space. A large part of threshold segmentation is dependent on MAs. This survey can be used as a valuable resource for developing multilevel thresholding techniques. The proposed survey can aid researchers in identifying areas for improvement and guiding future research. Research on multilevel thresholding techniques in information science can be improved by addressing challenges and limitations identified in this research. The paper is organized into sections that include an overview of existing studies, a literature review, analysis of datasets, open issues, and conclusions. The survey provides a detailed taxonomy of thresholding approaches, thresholding applications and case studies, and a description of existing image segmentation techniques. It also discusses simulation environments, programming languages, case studies, evaluation metrics, and research gaps.