The advancement of systems biology research has emphasized the capabilities of statistical analysis tools in distinguishing many factors associated with oil palm including genetic vs. environment (GxE) components from omics data. The availability of an efficient and robust ecometabolomics workflow has a high potential in augmenting oil palm precision agriculture. In this study, we employed cross-validation (CV) and receiver operating characteristic (ROC) methodologies to evaluate the performance of an oil palm metabolome dataset linked to GxE factors for its predictive ability and integrity. The specificity and sensitivity of identified metabolite candidates contributing to the demarcation of the two oil palm groups in the dataset were found to be distinctive and were of discrimination quality. The dataset showed no overfitting and exhibited excellent predictive power. This work provides fundamental information and a guideline for universal metabolome data exploration toward oil palm phenotyping and precision agriculture.