Cardiovascular disease (CVD) is the leading cause of global health issues which requires swift and accurate risk assessment techniques. Present-day risk calculators together with machine learning (ML) models have performed inadequately when it comes to reliability and comprehension of results. We produced a complex deep learning (DL) framework which builds Extended Long Short-Term Memory networks using an exponential gating mechanism (xLSTMeg). The new architecture design controls feature variations while extracting long-term dependencies through increased performance. A Random Forest Regression (RFR) evaluated the most important features while focusing on the top influential biomarkers, which eliminates redundant features. Performance is evaluated using cross-validation protocol and scientifically validated using DL explainability paradigm. The exponential gating mechanism achieved superior performance in CVD risk stratification by at least 6% compared to conventional long short-term memory (cLSTM) and other ML models. Analysis showed that RFR allows the system to benefits most from the 64% most significant features. The system yielded better results by employing DL algorithms which SHapley Additive exPlanations (SHAP) analysis proved essential features for risk prediction. Our study concludes that our proposed xLSTMeg architecture with optimized features for CVD risk assessment achieves better robustness and enhanced accuracy.