This paper provides a comprehensive survey of multi-level thresholding techniques in image processing, focusing on their application in image segmentation. The introduction highlights the importance of image segmentation in various fields such as object recognition, medical imaging, and robotics. Multi-level thresholding is discussed as a sophisticated method that captures the complexity and variability of images by dividing them into multiple intensity levels, which enhances the precision and adaptability of segmentation. The paper reviews various metaheuristic algorithms (MAs) used to optimize threshold values, including Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The review aims to identify future research areas, such as handling complex images, automatic threshold determination, and algorithm interpretation. The survey is structured into sections covering existing studies, the multi-level thresholding concept, a literature review, dataset analysis, and conclusions. The major contributions include a comprehensive review of related works and a detailed comparison of methods, their strengths, and weaknesses.This paper provides a comprehensive survey of multi-level thresholding techniques in image processing, focusing on their application in image segmentation. The introduction highlights the importance of image segmentation in various fields such as object recognition, medical imaging, and robotics. Multi-level thresholding is discussed as a sophisticated method that captures the complexity and variability of images by dividing them into multiple intensity levels, which enhances the precision and adaptability of segmentation. The paper reviews various metaheuristic algorithms (MAs) used to optimize threshold values, including Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The review aims to identify future research areas, such as handling complex images, automatic threshold determination, and algorithm interpretation. The survey is structured into sections covering existing studies, the multi-level thresholding concept, a literature review, dataset analysis, and conclusions. The major contributions include a comprehensive review of related works and a detailed comparison of methods, their strengths, and weaknesses.