<p>The rapid advancement in education and technology in various fields has enhanced the students’ future. The forecast of students’ educational performance paves the way for effective learning and training, aiming to improve their learning ability based on the prediction results. Various Deep Learning (DL) models demonstrated their effectiveness in the prediction process by analyzing the student data. The existing methods show their capability in prediction; however, they are not able to handle large datasets. To overcome this, a DL-based hybrid Pyramid Mobile Forward Harmonic Network (PyMFH-Net) model for student performance prediction is proposed. The data transformation is carried out initially by Yeo-Johnson, which makes the dataset more compatible. The essential features are identified through an ensemble feature selection process. Thereafter, the augmentation process enlarges the dataset, and the augmented features are fed into the PyMFH-Net model for the prediction process. The PyMFH-Net model’s performance is validated by using the evaluation metrics and compared with other existing methods. The results demonstrate that the PyMFH-Net model achieved superior performance for dataset 1, with a precision of 91.89%, a recall of 93.56%, and an F-measure of 92.71%, respectively.</p>