Rapid urbanization and land-use changes have significantly impacted urban thermal patterns. However, many studies rely on annual or summer-averaged Land Surface Temperature (LST) data, while analysis at the urban block scale, which is crucial for decentralized planning, has received less attention. To address this gap, this research presents an innovative approach to identify thermal anomalies at the urban block level in Qom, Iran, using wintertime Landsat 8 imagery over a ten-year period (2014–2023). The choice of the winter season, due to minimal vegetation cover, allows for a more accurate detection of physical changes and is also favorable for finding the probable energy waste over the city blocks. In this study, time series of outlier-free average and standard deviation values of Land Surface Temperature (LST) were extracted for 499 urban blocks. The probable outlier samples of each city block were found by Median Absolute Deviation (MAD) statistical test. Subsequently, by use of two different versions of the Reed-Xiaoli (RX) algorithm for anomaly detection, blocks with unusual thermal behavior were identified. The results indicated that the 22 identified blocks experienced significantly greater temperature variations (up to 9.07°C) compared to other blocks. Validation of these findings with Google Earth satellite imagery confirmed a direct correlation between the thermal anomalies and actual land-use changes, such as new construction or alterations in land covers. This method proved its effectiveness as a practical tool for monitoring urban developments for supporting decision-making in urban management.