In this study, we examine individual insurance amounts using health data. The performance of these algorithms has been compared using the three regression models employed in this study: multiple linear regression, decision tree regression, and decision tree regression. The dataset is used to train the models, and the training then assists in producing more predictions. Later, the model will be tested and verified by comparing the anticipated quantity with the actual data. These models' accuracy levels will then be compared. The decision tree and linear regression are outperformed by the random forest regression algorithm, according to the analysis. It enables a person to understand the required amount based on their health situation. They might examine any health insurance company, their plans, and the benefits while keeping in mind the anticipated amount from the project. Later, the predicted amount will be compared with the real amount. This can also be quite beneficial to someone who wants to concentrate more on the useful aspects of insurance than the health-related ones. In addition, most people are susceptible to being duped regarding the cost of insurance and may unnecessarily purchase expensive medical coverage. This project does not provide the precise sum needed by any health insurance provider, but it does provide a general sense of the sum needed by an individual for their personal health insurance. Prediction is inaccurate and does not apply to any organization; therefore, it should not be the only factor considered when choosing a health insurance plan. First, estimating the cost of health insurance is extremely beneficial and helps in better examining the amount required so that a person can be confident that the amount he or she is going to justify It can also provide you with a wonderful idea for maximizing your health insurance profits.