The primary goal of this study is to use efficient machine learning algorithms to anticipate better house prices, typically inflated. Materials and Methods: : This study will study the differences between near-accurate price prediction utilizing Novel Voting Regression (Group 2) and Decision Tree methods (Group 1). The sample size used to carry out this research was N=10 for each group studied. Clincle was used to calculate the sample size. The pre-test analysis was maintained at 80%. G-power is used to calculate the sample size. Statistical analysis yielded a significance value of 0.001. Results: : The accuracy of the Novel Voting Regression Algorithm for house price prediction is 82.94%, which is greater than the Decision Tree Algorithm’s 72.54%. The Independent Sample T-test has a statistical significance of 0.584. Conclusion: : As a result, it can be stated that the Novel Voting Regression technique can produce results that are almost as accurate as of the Decision Tree technique.