Prior prediction of various diseases such as cancer, diabetes, heart, lungs, etc. using Machine Learning (ML) algorithms have become a big boon in recent times. Lavish lifestyle and environmental pollution lead to occur fatal diseases viz. heart attack, cancer, asthma, etc. in the human body and may cause premature death. With the help of an ML predicting model, it’s easy to identify the disease without going to the hospital physically. ML is the subset of artificial intelligence, which helps to develop the intelligence ability in a system. In this work, we are providing a graphical interface or, web interface to the users, where they can feed their physiological symptoms and predict the diseases. Four supervised ML algorithms i.e., Random Forest, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are applied to the dataset to find the accuracy. The dataset is taken from Kaggle, consisting of 18 different features to predict multiple diseases like asthma, chicken pox, dengue, thyroid, etc. Results show that Random Forest performs well among others with 98% of accuracy. The accuracy scores of Logistic Regression (LR), Support Vector Machine (SVM) & K- Nearest Neighbor (KNN) are 91%, 90% & 82% respectively