Breast cancer is a significant global health issue, and early detection is vital for improving survival rates. The article reviews various studies investigating different methods for detecting breast cancer using non-invasive imaging techniques focusing on thermal imaging, including feature extraction, image segmentation, and machine learning. The proposed system aims to improve the accuracy and reliability of breast cancer detection by addressing the limitations of conventional infrared (IR) imaging approaches. By integrating innovative camera technologies, rotational thermography, and advanced data analysis techniques, the system overcomes challenges such as incomplete breast tissue coverage, limited adaptability to diverse patient profiles, and deficiencies in data analysis and diagnostic information. It concludes that developing efficient and accurate breast cancer screening software requires an integrated approach to data collection, image processing, and machine learning algorithms. The article presents a novel technique for developing breast cancer screening software using Rotational Thermo graphic Imaging, dynamic temperature-based data collection, Colour-based Infrared Image Processing, and a Machine Learning algorithm to provide complete breast imaging in a sitting position to reduce the chances of missing abnormalities. Image processing and machine learning techniques are utilised to extract a comprehensive, relevant feature set from the captured images and used to train a machine learning model. The system was tested on an increasing patient population in a clinical setting deployed at a hospital. The algorithm’s performance was evaluated using several metrics, including sensitivity (82.14%), specificity (98.33%), and accuracy (93.27%). The results demonstrate that the proposed algorithm achieved high accuracy and sensitivity, making it a promising tool for breast cancer screening.