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The real estate sector plays a crucial role in economic development, yet property price estimation remains a complex and challenging task due to the influence of multiple factors such as location, property size, number of bedrooms, and market demand. Traditional valuation methods largely depend on manual assessment, broker expertise, and static market comparisons, which often lead to inconsistent, time-consuming, and inaccurate price estimation. These challenges create uncertainty for buyers, sellers, and real estate professionals in making informed decisions. This project proposes a Real Estate Price Prediction System using Data Science and Machine Learning to provide accurate and real-time property price estimation. The system utilizes historical real estate data and applies machine learning techniques to analyse key property attributes including total square footage, number of bedrooms (BHK), number of bathrooms, and location. Feature engineering and data pre- processing techniques are employed to improve prediction accuracy and effectively handle location-based price variations. The proposed system integrates a trained machine learning model with a Flask-based backend and a web-based frontend interface. Users can input property details through a simple and user-friendly interface, and the system instantly predicts the estimated property price. The backend processes user inputs, performs real-time inference using the trained model, and returns reliable prediction results with low latency. The system ensures ease of use, scalability, and efficient deployment using lightweight and open-source technologies.