The chapter introduces a robust deep learning framework for predicting global ionospheric Total Electron Content (TEC), a critical parameter influencing the accuracy of Global Navigation Satellite Systems (GNSS).The model combines the convolutional layers for spatial feature extraction and GRU cells for temporal sequence modelling that appropriately captures the spatiotemporal dynamics of ionosphere behaviour. ConvGRU and Gated Recurrent Unit (GRU) models are used to enhance prediction accuracy. The GRU enables the model to focus on critical patterns within complex spatiotemporal data, addressing challenges associated with ionospheric variability due to solar and geomagnetic activity. The model was trained on comprehensive dataset of global historical TEC maps from Center for Orbit Determination in Europe (CODE), covering multiple layers of daily measurements. Using historicalionospheric TEC data, the proposed model outperformed conventional approaches in terms of ACCandNMI values during training and validation. Experimental results show that the proposed model consistently outperforms the traditional statistical and machine learning models, indicating it’s robustness across a wide range of ionospheric variations. The study provides actionable insights into improving GNSS reliability, particularly in low-latitude regions with highly dynamic ionospheric behavior. This work’s outcome could help to develop an ionospheric weather alert system for GNSS users. This work therefore brings forth the utility of AI-driven anomaly detection towards refining earthquake monitoring in India and supports a good tool for seismo-ionospheric research and early warning systems.