The Medical Insurance Expenditure Prediction Model, crafted through Linear Regression, accurately estimates an individual's insurance costs based on key parameters. Leveraging features like age, BMI, number of children, region, and smoking habits, this model assesses the expected medical expenditure. By training on a dataset encompassing diverse individual profiles and their corresponding medical insurance costs, the model learns relationships between these variables to deliver precise predictions. Linear Regression serves as the core algorithm, employing a linear approach to comprehend the relationship between the input features and the medical expenditure.