In the Turkish higher education system, make-up exams are a key mechanism enabling students to recover failed courses, yet the decision to take the make-up exam depends on multiple student- and course-level factors. This study develops an artificial neural network (ANN) model to predict whether students will take the make-up exam, using routinely available academic and course-related features. To obtain a comprehensive sample reflecting both student achievement and course characteristics, the dataset was drawn from courses spanning different academic levels and difficulty tiers. During data preparation, normalization and one-hot encoding were applied to facilitate model learning. Training, validation, and test subsets were formed using a Stratified Sample Projection in X and y (Stratified SPxy) sampling strategy to maintain representativeness across splits, and hyperparameters were optimized via random search to identify the best-performing configuration. The final model achieved 87.68% test accuracy and an 87.62% test F1-score, indicating good generalization. Notably, to the best of our knowledge, this is the first study to address make-up exam participation using only easily accessible institutional data, which makes the proposed approach practical and adaptable for real-world university settings. Despite relying on simple and readily available parameters, the ANN delivers strong predictive performance, suggesting that efficient models can be developed for non-complex datasets. Such predictions can enable early identification of at-risk students and trigger targeted academic support, inform course- and program-level adjustments, and improve operational planning, thereby contributing to more effective educational management and improved student progression and completion rates.