The proposed system introduces voice-based stock market guidance through presenting real stock prices and tomorrow’s trend forecast with natural language interactions. Suggested research attempts to fill the void between intricate analysis of financial information and natural human interfaces. The system supports NLP technology for extracting company names from user voice commands, fetches real stock data from Google Finance, and employs a Long Short-Term Memory (LSTM) neural network for predicting stock prices with around 85% prediction accuracy. The architecture employs a Python-based backend for data processing and prediction, Node.js for NLP execution and API calls, and offers an interface for users to interact with this application. Users can search past stock prices or forecast trends by voice commands or web interface, and the assistant responds through voice and text. This innovative strategy illustrates the ways in which combining NLP and machine learning can greatly maximize user interaction and productivity in finance applications, and that provides improvements over current approaches, which frequently handle data lookup or forecasting as individual issues.