The study presents an image processing-based analysis of the Foramen Magnum from skull images, employing a systematic methodology to extract crucial anatomical information. The proposed approach encompasses several key steps, starting with the acquisition of input skull images. Following image retrieval, the process involves converting the images to grayscale, thereby enhancing the focus on structural details. Subsequently, a Region of Interest (ROI) function is applied to isolate the specific area of interest within the skull image. The selected ROI is then displayed, allowing for visual confirmation and verification of the targeted anatomical region. The subsequent steps involve advanced image processing techniques, including edge detection using the Canny algorithm. To refine the detected edges and emphasize structural features, dilation and erosion operations are applied systematically. Contours of the prominent features, particularly the Foramen Magnum, are identified and sorted to facilitate a logical sequence in subsequent analyses. Drawing circles on the original points of the bounding box aids in visualizing and quantifying the key anatomical structures. The computation of midpoints further refines the analysis, providing geometric references crucial for accurate measurements. The study emphasizes the precise measurement of the Foramen Magnum, a critical anatomical landmark. Euclidean distances between midpoints are employed to calculate the size of the Foramen Magnum, contributing valuable quantitative data to the analysis. The final output image showcases the annotated midpoints, lines and measured dimensions, providing a comprehensive visual representation of the analyzed Foramen Magnum. We independently gathered a dataset comprising 430 skull images from Siddaganga Medical College and Research Institute in Tumakuru.