Random forest(RF) is a supervised machine learning approach that experts use to build and integrate many decision trees into a single forest. It takes considerable expertise to detect result anomalies depending on the degree of disparity between students CA and exam scores. It is doable to train RF-based classifiers to accurately identify anomalies with imbalanced data categorization. The aim is to develop RF-based classifiers capable of detecting abnormalities in student results, such as when a student performed remarkably well on the exam but poorly on the CA, or vice versa. The SMOTE technique was used to resolve unbalanced data categorization, which helped reduce dataset bias toward the majority class while also ensuring that the minority class received an acceptable sample size. Strong decision-makers were grouped into a class of majority vote using the grid search and randomized function. Trees capacity to learn from small data samples was arbitrarily constrained by the uniformly distribution function, which increased model accuracy and reduced tree correlation. Comparatively, the Classification, Adaboost, and GradientBoosting classifiers produced accuracy scores of 99.00%, 95.17%, and 81.50% respectively