## 1 Introduction
[PERSON] and [PERSON] advocated one-to-one correspondence between ionospheric anomalies and large earthquakes using integrated wavelet analysis [25; 26]. [PERSON], [PERSON], and [PERSON] discovered that ionospheric abnormalities, identified using a variety of ionospheric sounding techniques, can serve as reliable precursors to earthquakes [27; 28; 29]. [PERSON] reported unusual variation in the ionospheric F-region critical frequency prior to the Wenchuan earthquake, underlining the potential of ionospheric sounding for pre-earthquake identification [30]. [PERSON] at al. adopted multi-sensor analysis for ionospheric electron content and density determination [20]. Notably, regarding ionospheric plasma disturbances, [PERSON] et al. observed a significant increase in electron density (Ne) over the Kii Peninsula a full week prior to the earthquake [31]. [PERSON] and [PERSON] proposed the creation of a Swarm data-based ionospheric monitoring network as an earthquake early-warning system to assess the strong (Mw = 8.2) earthquake that struck Mexico on 7 September 2017 [32]. [PERSON] et al. observed that foF2 anomalies increased with earthquake magnitude but decreased with distance from the ionosonde station to the epicenter for Taiwan's 184 earthquakes (Mw > 5.0) from 1994 to 1999 [33]. [PERSON] et al. introduced a TEC time series forecasting model based on LSTM networks. Their findings confirmed the promising capabilities of LSTM in accurately predicting time series data [34]. [PERSON] used the Doppler sounding technique, employing the continuous monitoring of the Doppler frequency shift of reflected radio signals to investigate the ionosphere above earthquake sources [35]. Thereafter, [PERSON], [PERSON] and [PERSON] investigated and supported the correlation between seismic activity and ionospheric anomalies through the analysis of total electron content (TEC) fluctuations, and crustal stress (b-value) precursors to identify potential precursors and assess their effectiveness in short-term seismic prediction for the 2010 Mw 7.2 Mexico earthquake, the 2011 Mw 5.1 Lorca earthquake in Spain, and the 2022 Mw 7.7 Colima earthquake in Mexico [36; 37; 38]. In contrast to previously conducted studies on multi-parameter anomaly detection methods for seismic ionosphere disturbance [26; 39; 40], this study distinguishes itself in numerous significant ways. Firstly, it takes a comprehensive approach by combining multi-parameter anomaly detection algorithms, allowing for a more effective study of earthquake instances with an emphasis on temporally collocated ionospheric disturbances. Second, a simultaneous multi-platform approach is used, which includes a thorough examination of parameters such as TEC from GIMs along with ground-based GNSS stations, electron density, and temperature from Swarm satellites, as well as ionospheric height and critical frequency from ionosonde base stations. Additionally, the results collected from Swarm satellites are confirmed using ionograms. Finally, this study analyzes recent, severe, and unaddressed earthquake cases in Afghanistan, with the goal of finding the underlying geophysical phenomena that lead to earthquake hazards. By exploring these aspects, this study has the potential to provide valuable insights for earthquake prediction and contribute to the management of natural hazard risks in Afghanistan and neighboring regions. The integration of data from multiple platforms allows for complementary and mutually reinforcing verification at different spatiotemporal scales, enabling the precise identification of ionospheric anomalies. Involving the integrated use of GNSS, electromagnetic satellites, advanced machine learning techniques, and multi-parameter analysis aims to improve the detection, analysis, and prediction of ionospheric disturbances associated with earthquakes. Moreover, in this analysis, the cross-verification of TEC from multiple data sources such as IONOLAB and SIMRug is considered to be a necessity to exclude true anomalies [41; 42].