Anatomic stage of cancer is a key factor in cancer prognosis and provides vital information in almost all types of cancer. The anatomic stage used to represent stage of cancer which shows attributes of tumor - T, number of affected lymph node -N and distant metastasis - M. Experts retrieve the anatomic stage information from clinical and pathological reports and most of these are unstructured. The main aim of the research study is to propose generalized decision-making tool for most accurate anatomic stage extraction from medical reports of different health organizations. Most previous work considered single health organization for research; extracted only TNM value, did not identify anatomic stage from TNM value; used pathology reports only. This research addresses the extraction of anatomic stage from Breast Cancer medical reports using Natural Language Processing because most of these reports are unstructured and require text processing to extract stage information from them. To detect most accurate anatomic stage, TNM information will be extracted from medical reports using NLP, machine learning technique and rule-based methodology. Average T, N and M accuracy for the rural region is 98%, 94% and 99% respectively whereas for urban region, average accuracy is 95%, 88% and 98% respectively. The average stage accuracy for rural and urban region is 93% and 85% respectively. 87% average stage accuracy achieved in both regions. Anatomic stage extraction with high accuracy in different health organizations of different regions shows that the proposed study is promising and generalized for most accurate anatomic stage extraction. © 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.