In real estate, the ability to predict housing values is important to a number of stakeholders including potential buyers, sellers, realtors, and lending institutions. However, it can be seen that the common regression approaches are not very effective in executing such forecasts because property data is quite complex and dynamic in terms of its characteristics and social aspects. This study approaches the problem by reducing it to a classification problem by replacing each property with a collection of price percentiles. In this context, the use of machine learning techniques has permitted the scope of this research to develop a battle tested system that combines Support Vector Classifier, Random Forest Classifier, Gradient Boosting Classifier, and incorporates a new Stacking Ensemble Model into the fold. The Stacking Ensemble outperformed individual classifiers, achieving the lowest RMSE (175,250.47) and the highest R2 score (0.34), demonstrating superior accuracy and generalization. Due to the fusion of predictions from different classifiers, the Stacking Ensemble Model overcomes common issues such as overfitting, which was a complication for an individual classifier’s efficacy as well as being sensitive to hyperparameters. It is shown that such an approach leads to a better classification performance and also gives an understanding of how property prices behave in response to different factors. This research marks an important milestone in the field of machine learning applications in real estate as it brings a system that is cost-effective, data-driven, expandable and improves transparency of the market while aiding subsequent stakeholders in making better informed choices.