Aim: Enhancing accuracy in Skin Disease Prediction using Support Vector Machines over Random Forest. Materials and methods: Support Vector Machines algorithm and Random Forest algorithm with sample size (N=10) is executed with varying training and testing splits for predicting the accuracy for skin disease prediction. The performance of the classifiers is calculated based upon their accuracy rate using a skin image dataset. Results and Discussion: The accuracy of predicting skin disease using the Support Vector Machines algorithm (98%) and random forest (97.5%) is obtained. Their accuracies are compared with each other using different sample sizes also and a significance value of 0.263 (p>0.05). Conclusion: Prediction of skin disease using the SVM algorithm appears to be significantly better than the random forest algorithm with improved accuracy.