Abstract: The growing adoption of open and distance learning environments has resulted in the massive generation of educational data that require systematic and interpretable analysis. This study applies the Knowledge Discovery in Databases (KDD) framework to predict student academic performance using public datasets from Kaggle and the UCI Machine Learning Repository. Through the structured phases of KDD—data selection, preprocessing, transformation, data mining, and interpretation—the study demonstrates how methodical knowledge discovery enhances both predictive accuracy and explainability. Machine learning algorithms such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Neural Network (NN) were tested and compared. Results show that the Random Forest model achieved the highest accuracy (91.4%) and F1-score (0.89). The findings confirm that KDD is not merely a preprocessing mechanism but a methodological framework that transforms raw data into reliable, interpretable, and actionable educational insights.