Predicting ionospheric GPS Total Electron Content (TEC) data is quite challenging due to the need for processing large datasets and handling variations across time and space. Machine learning techniques have increasingly been adopted in recent years for predicting complex time series characterized by nonlinear and non-stationary behavior. This work emphases on using machine learning techniques to predict GPS-derived ionospheric TEC. A unique hybrid version combining EEMD (Ensemble Empirical Mode Decomposition), facebook’s Prophet, and BiLSTM is proposed to forecast TEC values for distinctive GNSS stations in India. The model demonstrated reliable performance during periods of both minimal and heightened geomagnetic activity. Its effectiveness is evaluated using various performance metrics and compared with existing models.