A basic task in image processing, segmentation is employed in several applications, including video analysis, medical imaging, and object detection. GrabCut and Otsu’s technique are two popular image segmentation methods. Otsu’s method is a straightforward, effective strategy that aims to enhance inter-class variation and decrease intra-class variance. It is based on histogram thresholding. For pictures with distinct foreground–background separation, it works very well. GrabCut, on the other hand, is a more advanced, iterative method that divides an image into foreground and background regions using graph cuts. It provides higher accuracy, particularly in difficult situations where the features of the foreground and background overlap or are not distinct. The performance of Otsu's approach and GrabCut is compared in this research using several measures, such as segmentation accuracy, computing efficiency, and adaptability to different image complexities. The study shows that GrabCut is better at handling more complex images, providing higher segmentation quality at the expense of more computational time, while Otsu's method excels in speed and simplicity, making it appropriate for applications with well-defined foregrounds and backgrounds. We examine the advantages and disadvantages of both algorithms, emphasizing the circumstances in which each works best. The study also makes recommendations for possible future research directions, such as hybrid strategies that combine the two methodologies to maximize GrabCut’s processing speed while utilizing each method’s advantages.