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Total Electron Content (TEC) is used for calculation of Ionospheric delay. The Precise forecast of TEC is useful to correct the range measurements. TEC depends on the time of measurement, solar radiation (SSN & F10.7), geomagnetic index (AP & KP), season and geographic location of the user. In this paper, Cokriging geostatistical method is applied to build the Surrogate Model (COKSM) to estimate the range error based on predicted Vertical Total Electron Content (VTEC). The model is tested with pseudo-range measurements of L5 & S band data received from the operational NavIC/GPS receiver positioned at ACSCE, Bangalore, India and also using L1& L2 data of IGS network station. In order to assess the developed model, we have predicted and analyzed the ACSCE-NavIC TEC and IISC-GPS TEC and found that COKSM has predicted well for both NavIC (ACSCE) and GPS (IISC) TEC. The average RMSE of COKSM for NavIC TEC prediction is 1.4920 TECU, mean accuracy is 1.1151 TECU and average correlation coefficient is 0.9854. For GPS TEC prediction COKSM yields average RMSE of 1.1435 TECU, mean accuracy as 0.9080 TECU and average correlation coefficient of 0.9926. The average range error of NavIC and GPS are 0.2126 and 0.0938 meters. In order to estimate the performance of COKSM, it is compared with the Median model, Fourier series, NTCM-GL, SPM, LSTM and TIEGM/WEIMER models. Based on the comparison results it is observed that COKSM predicts well than other prediction models and provides the RMSE of 2.1480 TECU, correlation coefficient as 0.9810 and mean accuracy of 1.4044 TECU, also COKSM performs 4.6% better than Median model, 26.10% better than Fourier series model, 56.44% better than NTCM-GL, 56% superior than SPM model and 4.43% better than LSTM model. Apart from this, COKSM performance is assessed by comparing the forecasting results with TIEGM/WEIMER model during the St. Patrick's storm and found that the average range error of COKSM is 1.65 m and TIEGM/Weimer model yields 2.06 m during the chosen period. These results indicate that COKSM gives better prediction results than other models and suitable for navigation applications. [ABSTRACT FROM AUTHOR]