The annual screening of breast is mandatory for every woman from the age of 40 to identify the Breast Cancer (BC) in the early stage. Premature detection and diagnosis will lower the death rate and increase their chances of survival. Screening the breast with Mammogram, Breast MRI and Ultrasound are invasive because of the rays emitted by the imaging modalities. Woman’s started preferring thermography based imaging modality which is non-invasive and painless screening. We proposed Thermography based Breast Cancer Detection model using Texture and Frequency (TBCD-TF) based features. Hand crafted descriptive features are extracted from the thermogram and the classification model is developed to identify the cancerous over the non-cancerous thermogram. Hundred Ten-fold cross validation and out of bag error analysis was performed to demonstrate the efficacy and explainability of the proposed model. The performance analysis of the TBCD-TF model is analyzed using benchmark DMR-IR dataset and achieved an accuracy of 99.4%.