The financial market is dynamic and highly volatile, making accurate and timely stock price prediction a challenging yet valuable task. This study presents an AI-Powered Real-Time Stock Price Estimator that leverages machine learning techniques to forecast stock prices based on live market data. The system integrates real-time data collection from financial APIs with predictive models such as LSTM (Long Short-Term Memory) networks, which are well-suited for time series forecasting. It processes historical stock data along with live feeds to continuously update predictions and provide users with near-instant insights into future price movements. The model is trained and evaluated using a range of performance metrics to ensure accuracy and responsiveness. This solution aims to assist traders, investors, and financial analysts in making informed decisions by combining the power of artificial intelligence with real-time data analysis. The initiative demonstrates the potential of AI in transforming traditional stock market forecasting into a more dynamic and adaptive process.