Aim: To enhance the accuracy in land price prediction using Random Forest Regression and Novel Lasso Regression. Materials and Methods: This study contains 2 groups i.e Random Forest Regression and Novel Lasso Regression. Each group consists of a sample size of 10 and the study parameters include alpha value 0.05, beta value 0.2, and the power value 0.8. Their accuracies are compared with each other using different sample sizes also. Results: The Random Forest is 88.39% more accurate than the Novel Lasso Regression of 75.44% in Land Price Prediction. The statistical significance difference (two-tailed) is 0.04 (p<0.05). Conclusion: The Random forest model is significantly better than the Novel Lasso Regression in identifying Land Price Prediction. It can be also considered as a better option for the House Price Prediction.