The use of linear regression for predicting housing prices is explored in this study. It makes use of a dataset that includes a variety of important variables, including place, size, and the number of bedrooms. Outliers and missing values are handled using data preparation procedures. To evaluate the model, the dataset is divided into training and testing sets. While regularization strategies manage overfitting, feature selection and engineering techniques isolate relevant predictors. The efficiency of linear regression models in somewhat accurate house price prediction is shown by experimental data. The research results offer real estate stakeholders’ knowledge that can be used to estimate home values and guide housing market decisionmaking.