<p>Asset allocation relies heavily on accurate volatility predictions due to the correlation between volatility and financial risk. The India VIX index is a gauge of the market’s risk assessment, and a precise estimation of India VIX prices can benefit financial risk mitigation. This research presents an ensemble deep learning framework that integrates long short-term memory (LSTM) and gated recurrent unit (GRU) structures to predict India VIX values. Hyperparameter tuning was carried out using random search to optimize performance, guaranteeing an effective exploration of the parameter range. Ensuring generalisability across various market situations, the proposed ensemble model was thoroughly examined using various measures, and robustness was proven using five-fold cross-validation. Experimental findings indicate that the ensemble LSTM-GRU approach surpasses independent deep learning methods, successfully predicting volatility patterns even amid market instability. The suggested model’s adaptability was further assessed using stress testing in unstable market circumstances, validating its reliability and consistent prediction performance. The Diebold–Mariano test confirmed that the ensemble model greatly surpassed all benchmark models, hence validating the results’ reliability. This research promotes the application of ensemble learning in finance, offering a means to improve forecast accuracy in evolving market scenarios.</p>