This paper analyzed the correlation between injection molding factors through correlation analysis. In addition, the decision-tree model, which is a white box model with excellent explanatory power, was used to obtain optimal molding conditions that satisfy multiple constraint conditions. First, 243 data to be used in the experiment were created through a full factorial design. Second, a correlation analysis was conducted to understand the correlation. Third, to verify the decision-tree model, the prediction performance was evaluated using RMSE. As a result, good prediction performance was confirmed. A decision-tree experiment analysis was conducted. As a result of the progress, the same results as the correlation analysis were derived. Based on the previous analysis results, optimal molding conditions were applied to CAE. As a result, the amount of deformation in the multi-cavity could be improved by about 1.1% and 2.72% while satisfying the constraint.