Aim: The aim of this research article is to elaborate the accuracy rate for detecting the spam comments on YouTube videos by using Innovative Support Vector Machine (SVM) in comparison with K-Nearest Neighbor (KNN) Classifier. Materials & Methods: The data set in this paper utilizes UCI machine learning repositories. The sample size of predicting the spam comments on YouTube videos with enhanced 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%. Predicting the spam comments on YouTube videos with enhanced accuracy rate is performed by Innovative Support Vector Machine (SVM) whereas number of samples (N=10) and K-Nearest Neighbor (KNN) where number of samples (N=10). Results and Discussion: The Innovative Support Vector Machine (SVM) classifier has 93.047 higher accuracy rates when compared to the accuracy rate of K-Nearest Neighbor (KNN) is 87.73. The study has a significance value of p<0.05 i.e. p=0.022. Conclusion: Innovative Support Vector Machine (SVM) provides the better outcomes in accuracy rate when compared to K-Nearest Neighbor (KNN) for detecting the spam comments on YouTube videos with enhanced accuracy rate.