The increasing complexity of interactions among financial markets, the expansion of information flows, and the mutual influence of stock and foreign exchange markets by economic and political factors have made volatility forecasting a significant challenge in the field of finance. Traditional volatility forecasting methods are primarily based on econometric and classical statistical models, which exhibit limited effectiveness when dealing with nonlinear relationships, dynamic cross-market dependencies, and large volumes of heterogeneous data. The present study aims to develop an intelligent model for the simultaneous prediction of stock market and foreign exchange market volatility using Generative Adversarial Networks (GANs) and multimodal learning techniques. In this research, a hybrid framework was designed by leveraging the capability of GANs to extract hidden patterns and generate realistic data representations, alongside the ability of multimodal learning to integrate financial, economic, and news-based information. The research dataset consists of historical stock market indices, exchange rates, macroeconomic variables, and textual financial news data collected during the period 2021–2025. Following data preprocessing, feature extraction, and synchronization of multi-source data, the proposed model was trained and its performance was compared with GARCH, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and XGBoost models. The findings indicate that the proposed model outperforms the benchmark models in terms of forecasting accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Furthermore, the model demonstrates a superior ability to identify complex dependencies and volatility spillover effects between stock and foreign exchange markets, enabling more accurate forecasts under varying market conditions. The results suggest that the integration of Generative Adversarial Networks and multimodal learning can significantly enhance investment decision-making, risk management practices, and the development of intelligent financial market analytics systems.