Accurate price forecast is essential for well-informed decision-making in the dynamic and ever-changing property market. Because they are unable to recognize complex market trends, current algorithms frequently fall short of providing accurate forecasts. A gradient boosting regression-based method for predicting home value that surpasses exceptional predictive accuracy is presented in this research. To choose the best model to use in this application, it compares a number of algorithms, such as random forest regression, support vector regression, decision tree regression, and linear regression. With an R2 of 0.9253, the suggested gradient boosting model outperformed other models in managing intricate interactions found in real estate datasets. Both buyers and sellers may learn from the findings, which enables them to make data-driven decisions in this competitive real estate market.