ABSTRACT The objective of proposed work is to predict the insurance charges of a person and identify those patients with health insurance policy and medical details weather they have any health issues or not. There are some types of health insurances, which are required to be predicted for a patient. The level of treatment in crisis department vary drastically depending the type of health insurance a person has by this we predict the insurance charges of a person In this paper, a multiple linear regression model for health insurance prediction is proposed. Some factors like age, gender, bmi, smoker, and children were input for developing the linear regression model. The model is very accurate considering that it works with real data from practice, with MAPE (Mean Absolute Percentage Error) around 3%, and coefficient of determination R2=0.7615896. This is significant improvement in comparison to traditional modelsin some recent investigations. The proposed model can be utilized for future health care purpose that process to facilitate the decision making process.