The determination of variance of surface air temperature is very essential since it has a direct impact on vegetation, environment and human livelihood. Forecast of surface air temperature is difficult because of the complex physical phenomenon and the random-like behavior of atmospheric system which influences the temperature event on the earth surface. In this study, forecast models based on artificial neural network (ANN) and genetic programming (GP) approaches were proposed to predict lead seven days minimum and maximum surface air temperature using the weather parameters observed at the station Chennai, India. The outcome of this study stated that models formulated using ANN approach are more accurate than genetic programming for all seven days with the highest coefficient of determination (R2), least mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) on deployment with independent test dataset. ANN models give statistically acceptable mean absolute error of 0.59oC for lead day one in minimum temperature forecast and 0.86oC variance for lead day one in maximum temperature forecast. The study also clarified that the level of accuracy of the proposed prediction models were found to be better for smaller lead days when compared with higher lead days with both approaches.