Total Electron Content (TEC) of the ionosphere under fluctuating geomagnetic conditions plays a pivotal role in space-based communication and navigation systems. Periods of geomagnetic storms have a significant effect on the ionospheric TEC, thereby impacting the precision of Global Navigation Satellite Systems (GNSS) as well as other communication systems. This study evaluates the performance of three predictive models: Diffusion, Transformer, and Seasonal Autoregressive Integrated Moving Average (SARIMA) by forecasting TEC during different geomagnetic storm conditions. Diffusion model predicts by simulating the random processes that govern ionospheric variability, and SARIMA model relies on statistical principles to emulate seasonality, trend, and autocorrelation structure within data. Our study uses TEC and geomagnetic data to differentiate between storm and quiet periods, evaluating each model’s predictive accuracy in these distinct scenarios. Performance was benchmarked against the Center for Orbit Determination in Europe’s (C1PG) 1-day ahead ionospheric forecasts. This study specifically concentrated on the performance of the prediction models within a singular grid cell located at the exact geographical coordinates of 114.3° E longitude and 30.5° N latitude. The study indicates that all three models exhibit exceptional forecasting abilities during both geomagnetic storm and quiet periods. Significantly, Diffusion Model surpasses the others, achieving an outstanding 85.64\% of its predictions within the high-correlation interval ranging from 0.95 to 1.00 during quiet geomagnetic periods, while the C1PG model records 51.66%. Otherwise, during strong geomagnetic storm periods, Diffusion model operates at a 75.41% accuracy rate, while the C1PG achieves a 71.77% accuracy rate.