Pelvic bone tumors are difficult to locate on radiographs because of the low sensitivity of basic radiographic detection. Additionally, a simple radiograph cannot show cortical destruction, significant periosteal reaction, or a major soft tissue component, with or without calcification. CT or MRI is found to be better at characterizing tumors and extracting many radiographic features. The most important tool is an MRI, which is utilized for diagnosis, therapy monitoring, and recurrence detection. But a biopsy is required to get a more precise diagnosis. It was discovered that there are not many databases dedicated to these cancers. Building a database with all the radiographic characteristics of cancers was motivated by this study. As a result, the study's main objective is to make review research on pelvic bone tumors to build this database. Creating a taxonomy that organizes past studies on pelvic bone tumors is another objective. In this study, a certain research approach that the researchers had already authorized was applied. As a result, we were able to produce tables that show a tumor's radiographic features and whether it is benign or malignant. If the tumor is malignant, these tables also show its stage. This research is a component of the development of diagnostics using artificial intelligence as the type of tumor and its stage may be determined by artificial intelligence using this database through deep learning or machine learning, which will make the work of doctors easier with accuracy 100%.