Ground-level ozone (O3) is a secondary pollutant and has an adverse effect on human health, agriculture and ecosystems. The aim of this study is to develop model and to predict future O3 concentrations level in Shah Alam for next day (D+1), next two days (D+2) and next three days (D+3) using traditional method of Multiple Linear Regression (MLR) based on the concept of Ordinary Least Square estimate (OLS). This study uses daily average data of air pollutants (O3, NOx, NO, SO2, NO2, CO) and meteorological variables (WS, T, RH) that was selected from 2002 until 2013 as independent variables. The performance indicator of the models are measured by accuracy measures (Prediction accuracy, Index agreement and Coefficient of determination) and error measures (Root mean square error, Normalized absolute value). The average accuracy measures (AI, PA and R2) show that the prediction for D+1, D+2 and D+3 is 0.4492, 0.3797 and 0.304 respectively. Meanwhile, the average error measures (RMSE, NAE) show that the prediction for D+1, D+2 and D+3 is 0.1453, 0.1374 and 0.1302, respectively.