Machine learning has had a significant impact on image recognition, product recommendation, clinical diagnosis, and nearly every other field of technology. Every aspect of our life is surrounded by machine learning algorithms that help to improve security, public safety, medicine, transportation, and so on. Because of the growth in urbanization and the volatility of property values, there is a greater demand for a system that predicts property prices. Real estate price prediction can assist in resolving this issue and forecasting house prices so that customers can examine them. This paper reflects the effort made to solve the aforementioned challenge. This study uses machine learning algorithms as a research technique to create models for predicting house prices. We developed a combined feature selection based model for predicting housing costs, look at the precision of the models including linear regression, lasso regression, random forest and decision tree. Such models are used to create a predictive model and to select the best performing model by comparing the prediction errors derived from various models, and the analysis shows that the linear regression algorithm consistently outperforms other methods in terms of accuracy, which comes at 84.78. The authors attempt to construct a user-friendly interface design that will allow consumers to select from their options based on their needs and receive an estimated pricing for the property.