Aim: The main objective of this research article is to improve the accuracy rate in the Novel prediction of quality of rice in rice mills by using Decision Tree (D-Tree) in comparison with Support Vector Machine (SVM) Classifier. Materials & Methods: The data set in this paper utilizes the publicly available Kaggle data set for Novel prediction of the quality of rice in rice mills. The sample size of Novel prediction of quality of rice in rice mill with improved accuracy rate was sample 80 (Group 1=40 and Group 2 =40), 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%. Novel Prediction of quality of rice in rice mill with improved accuracy rate is performed by Decision Tree (D-Tree) whereas some samples (N=10) and Support Vector Machine (SVM) were the number of samples (N=10). Results: The Decision Tree (D-Tree) classifier has 92.7 higher accuracy rates when compared to the accuracy rate of Support Vector Machine (SVM) is 91.0. The study has a significance value of p<0.05, i.e., p=0.035 which infers they are statistically significant. Conclusion: Decision Tree (D-Tree) provides better outcomes in accuracy rate when compared to Support Vector Machine (SVM) for Noval prediction of quality of rice in rice mill.