Automation of cephalometric landmark annotation holds significant importance within the domain as a research area. This article presents a dataset consisting of 1692 cephalometric radiograph images, along with manually annotated data on 19 anatomical cephalometric landmarks. The data is available in individual text files corresponding to individual radiographic image files. Along with this, the latest annotation formats like COCO JSON and YOLO file formats for artificial intelligence (AI) model training are also available. Annotations are performed by experienced medical professionals with more than twenty years of experience and technical professionals with 6 years of research experience in the domain. The images are pre-processed and segregated based on resolution, image quality, dental structure, and other artifacts. The final image data is saved in BMP format with 0.127 mm/pixel resolution. The images refer to cephalometric analysis performed between 2011 and 2022 in patients treated at JSS Dental College and Hospital (JSS DCH), with patients age between 5 to 60 years. The demographic information of patients is not available with individual image data. Ethical clearance is obtained from the Institutional Ethical Committee of JSS DCH. Various levels of experimentation are carried out using the dataset, and the results demonstrate robust performance. Landmark annotation based on segregation type is one of the first types in this area of cephalometric landmark annotation. The availability of this dataset offers researchers a robust platform for investigating and conducting experiments using machine learning and deep learning techniques.