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Machine Learning, particularly Polynomial Ridge Regression and Random Forest Classifier, is used to predict medical insurance costs with accuracy and personalization. Polynomial Ridge Regression models complex relationships for cost estimation, while Random Forest Classifier categorizes individuals into cost groups. Key factors like age, BMI, smoking, and region serve as inputs. Data preprocessing ensures accuracy, while real-time feedback enhances predictions. Feature importance analysis helps assess financial risks. With scalability and a user-friendly interface, the system supports transparent, reliable, and adaptive insurance cost management.