In this research study, the author analyzes data set containing a large number of scores to estimate the feasibility of applying artificial neural networks or ANNs for outcome and performance prediction. This study aims at developing an ANN model to forecast final grades with a satisfaction of predictive accuracy by incorporating several parameters consisting of race and ethnicity, involvement ratings, number of absences, special education status, and exams and home word results. Respective analysis of the dataset was done whereby non-numerical features were immediately encoded, normalization was conducted and managing of missing features among others were conducted. The efficacy of the two hidden layer ANN model was evaluated using the conventional 80:20 training–testing data division. We got an coefficient squared (R2) value of 0.490 with a Mean Squared regression Error (MSE) of 0. This study’s model proved to have moderate predictive accuracy as shown by the model 0.033. The classroom rules such as homework assignments and the punctuality or truancy of learners and their participation gained credence. Altogether, the ability to increase the amount of data and examine individual students made analyzing the effect of feature score changes on further performance possible, thus proving the effectiveness of the proposed model. The findings reveal the benefits to be received by activity participation, class attendance monitoring, and homework solutions. This work contributes to the growing literature on data mining for education and suggests future work including the introduction of more attributes, the exploration of higher level experiments with the structure of artificial neural networks and longitudinal studies. Through applying these tactics, evidence-based decision making in education processes and, as a result, the outcomes of students’ learning can be enhanced.