Detecting ionospheric anomalies constitutes a key research focus in the field of early seismic warnings and responses. This study investigates pre- and post-seismic ionospheric anomalies in relation to strong earthquakes, utilizing Global Ionospheric Map (GIM) data and applying three detection methods: the interquartile range (IQR), first-order and second-order numerical difference (FSND), and butterworth bandpass filter (BBF). The study focuses on two cases of strong earthquakes (inland and offshore earthquakes): the Mw 7.1 earthquake in Aksu, Xinjiang, China (January 23, 2024), and the Mw 7.4 earthquake off the west coast of Honshu, Japan (January 1, 2024). The ionospheric total electron content (TEC) disturbances in these events were detected and analyzed, and the detection performances of the three methods were compared. The results show that all three methods effectively capture ionospheric TEC disturbances and provide valuable insights into earthquake precursors and epicentral anomalies. The IQR method detected pre-seismic disturbances for both inland and offshore earthquakes. The FSND method consistently identifies “N”-type disturbances in the Differential TEC (DTEC) for both events. The FSDN method can process long-term TEC time series, whereas the BBF method is restricted to smaller datasets. In practice, the BBF experiment examined TEC data on a daily basis to identify ionospheric dis-turbances.The BBF method also highlights “N”-type disturbances by employing 30-minute TEC data, unlike the other two methods that used 1-hour sampled data. These findings offer theoretical insights into ionospheric responses to earthquakes and have practical implications for earthquake prediction.