The objective of this research, “Feature Engineering and Predictive Modeling for Accurate House Price Valuation,” is to establish a thorough strategy for estimating the value of homes by utilizing cutting-edge machine learning techniques. The paper posits that feature engineering is essential for the acquisition of valuable data from housing datasets by converting fundamental attributes such as location, size, number of rooms, amenities, and neighborhood elements into predictive features. A diverse array of methodologies, such as Gradient Boosting, Linear Regression, Decision Trees, and Random Forest, are implemented and evaluated in order to ascertain the most precise valuation model. The paper’s primary objective is to improve the accuracy of predictions and reduce valuation errors in the real estate industry, thereby facilitating data-driven decision-making. The results demonstrate that the performance of the model is significantly enhanced by the use of carefully selected features and variables. Consequently, more precise and scalable house price estimation systems are achievable.