An apparatus for analyzing a cephalometric image, the apparatus comprising: a control unit configured to extract a landmark point on a cephalometric image, and to generate an analysis image by combining the landmark point with an identification image; and memory configured to store the generated analysis image, wherein for extracting the landmark point the control unit is further configured to set an area of interest on the cephalometric image, and to identify a first point, assumed to be the landmark point, by performing machine learning for the area of interest, characterized in that the control unit is further configured to acquire the cephalometric image from a raw image which is including not only the cephalometric image but also an image of another body part of a patient by extracting coordinates corresponding to a plurality of identifiers from the raw image and setting an area, including not only the identifiers but also predetermined ranges around the respective identifiers, as the cephalometric image, the control unit is further configured to set the area of interest by detecting an area, determined to be a medically important point, by using machine learning and marking with a bounding box, the control unit is further configured to perform geometric computation to verify the area of interest by using an organ structure near the area of interest, and the control unit is further configured to perform machine learning for the area of interest by generating a probability image map by providing a learning model for points to be extracted as landmark points, performing segmentation by means of the provided learning model, setting blobs on the generated probability map, and extracting center points of the blobs as landmark points. a control unit configured to extract a landmark point on a cephalometric image, and to generate an analysis image by combining the landmark point with an identification image; and memory configured to store the generated analysis image, wherein for extracting the landmark point the control unit is further configured to set an area of interest on the cephalometric image, and to identify a first point, assumed to be the landmark point, by performing machine learning for the area of interest, characterized in that the control unit is further configured to acquire the cephalometric image from a raw image which is including not only the cephalometric image but also an image of another body part of a patient by extracting coordinates corresponding to a plurality of identifiers from the raw image and setting an area, including not only the identifiers but also predetermined ranges around the respective identifiers, as the cephalometric image, the control unit is further configured to set the area of interest by detecting an area, determined to be a medically important point, by using machine learning and marking with a bounding box, the control unit is further configured to perform geometric computation to verify the area of interest by using an organ structure near the area of interest, and the control unit is further configured to perform machine learning for the area of interest by generating a probability image map by providing a learning model for points to be extracted as landmark points, performing segmentation by means of the provided learning model, setting blobs on the generated probability map, and extracting center points of the blobs as landmark points. <EOS>