The cryptocurrency market is considered one of the most volatile financial markets due to its decentralized nature, high liquidity, and strong sensitivity to news and social events. Accurate prediction of market volatility can significantly support investment decisions, risk management, and the effectiveness of trading strategies. This study proposes a hybrid framework that integrates deep learning techniques with social media sentiment analysis to predict cryptocurrency market volatility. Historical price and trading volume data are combined with textual information extracted from social media platforms such as Twitter, Reddit, and Telegram. Natural Language Processing (NLP) methods are employed to extract user sentiments, while deep neural network architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are utilized for time-series forecasting. The findings indicate that integrating financial indicators with sentiment-based features significantly improves prediction accuracy compared to models relying solely on historical market data. Furthermore, the results reveal that social media sentiment possesses substantial explanatory power regarding future cryptocurrency market behavior. The proposed framework offers valuable implications for investors, financial analysts, and developers of intelligent trading systems seeking to enhance forecasting performance in highly dynamic digital asset markets.