The production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation of every known gene related to a disease. This issue is further elevated when different variations of diseases are considered. As such the use of different computational methods are considered to tackle this issue. This research makes use of different machine learning algorithms in the identification and prediction of Single Nucleotide Polymorphism. This research presents that Gradient Boosting algorithm performs better in comparison to other algorithms in genic variation predictions with an accuracy of 70%.