Image segmentation techniques divide an image into multiple segments to simplify its analysis, making it easier to identify objects, boundaries, or regions of interest. This process is essential for applications requiring detailed image analysis, such as medical diagnostics, autonomous driving, and satellite imagery interpretation. In medical imaging, for example, segmentation is used to isolate areas like tumors, organs, or tissues for further analysis or treatment planning. Image segmentation techniques are categorized into methods such as thresholding, edge detection, region-based segmentation, and deep learning approaches like fully convolutional networks (FCNs) and U-Net architectures. These methods enhance the precision of tasks like object detection, classification, and scene understanding. As deep learning continues to advance, segmentation models are becoming more accurate, even in complex and high-resolution images. With the integration of cloud-based processing, these techniques can handle large datasets efficiently, enabling real-time analysis for applications like autonomous vehicles, where quick and accurate segmentation of the environment is critical for safe operation.