Aim: The main objective of this research article is to improve the accuracy rate in Novel prediction of quality of rice in Rice Mill by using Support Vector Machine (SVM) compared to Enhanced Convolutional Neural Networks (ECNN) Classifier. Materials & Methods: The data set in this paper utilizes the publicly available Kaggle data set for Novel prediction of 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 Gpower 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 Support Vector Machine (SVM) whereas the number of samples (N=10) and Enhanced Convolutional Neural Networks (ECNN) where some samples (N=10). Results: The Support Vector Machine (SVM) classifier has 91.0 higher accuracy rates when compared to the accuracy rate of Enhanced Convolutional Neural Networks (ECNN) is 90.44. The study has a significance value of (p<0.05), i.e., p=0.044. Conclusion:Support Vector Machine (SVM) provides better outcomes in accuracy rate when compared to Enhanced Convolutional Neural Networks (ECNN) for Novel prediction of quality of rice in rice mills.