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There is a life after breast cancer. The prerequisite is early detection. Breast cancer is curable when detected early, tiny, and has not spread – regular screening aids in early detection. Clinical Thermography and artificial intelligence are potentially a good fit for early breast cancer screening. This survey paper presents a systematic review of artificial intelligence-based breast cancer screening using thermal infrared cameras. Initially, we will present the qualitative analysis of the existing literature regarding the trend and distribution. This review manuscript will then explore the literature about infrared thermal image acquisition and storage techniques. We will then highlight various segmentation techniques used for processing infrared thermal images. This paper presents the experimental results of the traditional image processing and deep learning-based segmentation techniques available in the literature using infrared breast thermal images. We then summarize the works that have used artificial intelligence to segment and classify infrared thermal images. The existing literature shows opportunities to explore the area of explainable artificial intelligence (AI). Explainable AI will make clinical Thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]