This project implements a predictive analytics web application for estimating residential property values, focusing on both house price prediction and land price prediction. At its core, the system integrates machine learning-driven valuation models with a backend API layer, enabling structured inputs (such as area, location attributes, built-up specification, and land metrics) to produce reliable price estimates. The solution combines data-driven modeling with a scalable server architecture, delivering an end-to-end forecasting capability that supports real estate decision-making.