There are many variables involved in the real life problem so it is difficult to choose an efficient model out of all possible models relating to analytical factors. Interaction terms affecting the model also need to be addressed because of its vital role in the actual dataset. The current study focused on efficient model selection for collector efficiency of solar dryer. For this purpose, collector efficiency of solar dryer was used as a dependent variable with time, inlet temperature, collector average temperature and solar radiation as independent variables. Hybrid of the least absolute shrinkage and selection operator (LASSO) and robust regression were proposed for the identification of efficient model selection. The comparison was made with the ordinary least square (OLS) after performing a multicollinearity and coefficient test and with a ridge regression analysis. The final selected model was obtained using eight selection criteria (8SC). To forecast the efficient model, the mean absolute percentage error (MAPE) was used. As compared to other methods, the proposed method provides a more efficient model with minimum MAPE.