Aim: To perform the best route analysis using a novel Random forest algorithm compared with Bagging tree classifier. Material and Methods: The data set in this paper utilizes the publicly available Kaggle data set. The sample size of a best route prediction system with improved accuracy rate was sample 20 (Group 1=10 and Group 2=10) and calculation is performed utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. The best route analysis is performed by using Random forest (RF) with a number of samples (N=10) and Bagging tree (BT) Classifier with a number of samples (N=10) respectively. Results: The novel Random forest algorithm has 95.00 percent higher accuracy rates when compared to the accuracy rate of BT is 88.33 percent. The study has a significance value of p<0.05 i.e. p=0.021. Conclusion: The proposed random forest achieved significantly better classification than the bagging classifier for predicting the best route analysis.