Medical diagnosis is a category of medical tests designed for disease or infection detection. Early medical diagnosis of disease is crucial to increase the chances of recovery for the patient while errors in medical diagnosis has been proven to be fatal in most cases. Machine learning is a practical approach for medical diagnosis with minimum cost and high accuracy because machine learning algorithm implementations are making near-perfect diagnosis of diseases, recommending best medicines and identifying high-risk patients. For countries like Nepal, where 13.02 million people are at risk of malarial infection every summer [1], machine learning can help reduce cost of treatment and provide early and accurate diagnosis of the disease which can prove vital for saving many lives. In this project, I use image processing tools to convert pathological data in captured images to machine readable form and apply machine learning algorithms to diagnose infected cells. Out of all the algorithms, CNN achieved highest test accuracy of 96.24% while KNN achieved least test accuracy of 58%.