In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often struggle to capture the temporal dynamics inherent in student data, such as learning trajectories, study habits, and evolving performance patterns. In response, this research leverages Recurrent Neural Network (RNNs) and Long Short Term Memory (LSTM) network (LSTMs), which are specifically designed to model sequences and long-term dependencies from OULAD and self-generated Emotional dataset. By incorporating these architectures, the proposed methodology excels in capturing the intricate relationships between various factors over time. Further, various ML models such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) are integrated with RNN + LSTM to enhance the predictive power of model. The proposed system with RNN + LSTM + RF techniques gained approximately 97% accuracy that is comparatively higher than RNN + LSTM + SVM, RNN + LSTM + NB and RNN + LSTM + DT i.e., 90.67%, 86.45% & 84.42% respectively.