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This paper presents a machine learning-based system for predicting medical insurance costs. The system utilizes a dataset from Kaggle containing 1,338 entries with features such as Age, Gender, BMI, Smoking Habit, and number of children. The study aims to enhance the efficiency of insurance policies through advanced predictive modeling, particularly in the context of the Covid-19 pandemic. Various regression models, including Linear Regression, Random Forest, and Gradient Boosting, were employed. The models were trained on a 70-30 dataset split and evaluated for accuracy. Random Forest emerged as the top performer with an R-squared value of 0.87, followed closely by Gradient Boosting (0.85). The results underscore the potential of machine learning to refine insurance pricing by leveraging personal and regional health data for more accurate predictions