Today's educational environment considers student academic performance prediction to be extremely important in educational organizations. It is a key problem for an academic system at all educational stages. Every educational system that wants to enhance its students' studying experience and academic success must be able to forecast their academic performance. The majority of current research on predicting student performance uses traditional feature selection strategies that involve extracting characteristics and feeding them to a classifier. Scholars can now extract meaningful high-level features from unprocessed data thanks to deep learning (DL). Performance evaluation on difficult tasks is made possible by such sophisticated feature selection strategies. This work proposed a combined Triple Voter Network and t-Self Improved Distribution-based Satin Bowerbird Optimization (t-SIDSBO) predict student academic achievement. Here, the deep LSTM model, CNN model, RNN model which are based on advanced feature prediction models, is used for effective classification, and the best features are chosen using a t-SIDSBO-based feature selection strategy. In this paper, the detailed explanation of the student academic prediction and the feature selection of t-SIDSBO using DL is explained step by step procedure. Following that, the anticipated performance is assessed and improved using performance metrics like accuracy, F_ score, recall, specificity, and precision. The program is implemented using the Python platform.