This research covers a new paradigm in predicting the Big Five personality traits relevant to industrial applications using ensemble machine learning models with diverse cognitive and behavioral metrics in sequence. This study also integrates emotional intelligence or EQ, intelligence quotient or IQ, risk-taking behavior, and traditional physiological frameworks in order to construct a multidimensional model. The data used for the analysis came from a total of 406 participants in the US and India, where SMOTE was applied to address issues of class imbalance. The machine learning models analyzed sequentially are Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, and Nearest Neighbor. The ensemble combination of LR → DT → RF → SVC → NN yielded the highest cross-validated accuracy of 61%. This study demonstrated the potential of the proposed sequential ensemble models for robust personality trait prediction and could be used in practical applications like recruitment, leadership development, and customized employee training.