The real estate industry makes extensive use of data mining. Because data mining may extract valuable information derived from raw data, which is highly helpful for determining important concentrating characteristics, property values, and many other things. According to a study, owners as well as the real estate industry are frequently concerned about fluctuations in prices. To examine the pertinent characteristics and finest models for predicting house values, a review of the literature is conducted. The results of this research indicated that the most effective models, when compared to other models, were the decision tree, lasso regression, and linear regression. Furthermore, the conclusions of our study also imply that location and structural factors represent a significant role in predicting a home’s value. This study will aid housing developers and academics by identifying the most important factors influencing the price of homes and suggesting the most effective machine learning model for further study on this particular subject.