Breast cancer is one of the most common and deadly cancers, and mucos affects women and men. Thorough prognosis and specificity in turnaround time have improved vastly. There are several breast cancer screening modalities, each with specific benefits and drawbacks. Breast diseases identified through such technologies include breast cancers; of these, one of the non-invasive, pain-free technologies that detect breast cancer through abnormal thermal pattern recognition is Thermography. The combination of thermography along with modern image processing and smart classification algorithms has contributed substantially to the progress in early diagnosis. In this paper, we investigate the capabilities of thermography, numerical modeling, and artificial intelligence (AI) as a synergistic combination for the detection of breast cancer in its incipient stages. Covered with community-wide screenings combined with the use of cheaper, non-contact broadband devices, these technologies could provide a rapid and accessible diagnosis of breast cancer. The article reviews recent developments in AI-powered thermographic analysis, explaining its contribution to improving detection accuracy and solving current challenges, thus creating a baseline for future investigation in the early diagnosis of breast cancer.