This project utilizes a Multi-Layer Perceptron (MLP) Neural Network to predict the academic performance of students based on demographic information, study habits absenteeism and parental education levels for their text scores. The MLP model was trained using students’ records, which underwent pre-processing steps including normalization and categorical encoding to prepare the data for analysis. The model was evaluated based on its ability to predict students’ Grade Point Average (GPA), using performance metrics such as Mean Squared Error (MSE) and R-squared (R2). The model achieved an R2 score of 0.71, indicating that it successfully captured a significant portion of the variability in academic performance. Key predictors of academic performance included study hours, test scores, and absenteeism, while the model encountered challenges in predicting outcomes for students with irregular academic behaviours. This research demonstrates the potential of Multi-Layer Perceptron neural networks in predicting student academic outcomes and suggests future improvements by incorporating larger datasets and more diverse features to enhance prediction accuracy and applicability.