In this study, Gaussian Process Regression (GPR) is employed for forecasting low-latitude ionospheric conditions. The GPS receiver data from the International GNSS Services (IGS) station in Bengaluru, India, is used for 8 years (2009–2016) during the 24th solar cycle. The performance of the GPR model is evaluated using statistical parameters such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and correlation coefficient. The results of the proposed GPR model are compared with those of the Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model during the solar maximum period and descending phase of the 24th solar cycle. The experimental results demonstrate that the GPR model significantly outperforms the ARMA and ANN models in forecasting ionospheric time delays for GNSS signals. The outcomes of this work hold promise for developing a web-based Ionospheric TEC forecasting system to provide alerts to GNSS users.