One of the most prevalent types of cancer worldwide is breast cancer, a condition that tends to change the thermal pattern of the breasts. Thermographic images, a functional examination that considers temperature variation, emerge as an alternative for this disease since they consider body temperature variation to investigate anomalies. This examination has been widely investigated in studies for screening or diagnosing breast cancer. However, a limited number of studies investigate this examination to track the progress of cancer and assess tumor response to treatment. This study works in this context, exploring thermography during neoadjuvant treatment. In the proposed methodology, we first preprocess the thermal data and use the k-means unsupervised learning algorithm to identify the hottest regions. Subsequently, we build time series based on statistical measures and homogeneity measures among thermal captures to evaluate the patients’ tumor evolution. The results show that this approach indicates the treatment evolution correctly in at least 79% of the cases when observing only the statistical measures and 95% of the cases when combining the statistical and homogeneity measures on the patient evaluation process.