The ability to predict building responses subjected to an earthquake could be used to identify building damage which would largely reduce human inspection effort and operation downtime. This thesis explores various of machine learning methods to formulate prediction model for seismic building responses over the great Los Angeles region using three actual earthquake scenario data (1994 Northridge, USA, 1999 Chi-Chi, Taiwan and 2000 Tottori, Japan). The result shows that the geospatial interpolation method kriging outperforms other candidates among all earthquakes in both accuracy and model stability using criteria such as cross-validation and median absolute residual difference. Some inconsistency in accuracy levels between different earthquakes are caused by 1)earthquake characteristics and 2)representativeness of data samples of each event.