ABSTRACT This paper introduced HyperVIX, a novel hybrid framework that integrates ARIMA modeling, LSTM neural networks, and Gray Wolf Optimizer (GWO) to forecast the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Using a multilayered approach, HyperVIX first employs ARIMA to capture linear time series patterns, followed by LSTM networks that model the residuals to identify complex nonlinear relationships. The GWO algorithm optimizes the LSTM hyperparameters, enhancing the framework's ability to capture Volatility Index (VIX)'s intricate dynamics. Empirical analysis demonstrates that HyperVIX significantly outperforms both traditional and contemporary financial forecasting models in terms of accuracy and robustness. Compared to single models, HyperVIX achieves approximately 15%, 12%, and 10% improvements in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics respectively, with the R 2 value increasing by about 5%. Notably, the model exhibits exceptional performance during extreme market volatility periods, making it particularly valuable for risk management applications. This research contributes to the literature by providing an innovative and effective method for VIX forecasting while offering valuable insights for financial market volatility analysis and investment strategy optimization.