The regression analysis plays a vital role in forecasting, estimating and predicting the material science domain. In this research work measures a statistical model to estimate the critical temperature of superconductor. This critical temperature formulated by using the superconductor’s chemical formula. The statistical model has given several measurements like Correlation Co efficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSR), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). These measurements extracted based on atomic mass (AM), atomic radius (AR), valence(V), thermal conductivity (TC), and electron affinity (EA) contribute the most to the model’s predictive accuracy. This research work focuses the comparisons of various measurements namely Correlation Co efficient, Mean absolute error, Root Mean squared error, Relative absolute error, Root relative squared error and also time taken to build the model of leading regression algorithms like Linear and Simple Linear regression models for superconductor.