This study investigates the influence of demographic and lifestyle factors on healthcare charges using a multivariate regression approach. The analysis utilizes a dataset containing variables such as age, sex, BMI (Body Mass Index), number of children, smoking status, and region, with healthcare charges as the dependent variable. The results reveal that age, BMI, smoking status, number of children, and region significantly impact healthcare costs, while sex does not emerge as a significant predictor. Smoking status is identified as the most impactful factor, highlighting the financial burden of smoking-related health issues. The model demonstrates strong predictive power, explaining 75% of the variance in healthcare charges. These findings align with existing literature and underscore the importance of integrating demographic and lifestyle variables into predictive frameworks. The study offers valuable insights for insurers and policymakers, suggesting targeted interventions, such as obesity prevention and smoking cessation programs, and addressing regional disparities to promote equitable healthcare access. This research contributes to the field by providing a comprehensive analysis of these factors within a single predictive model, with implications for healthcare cost management and policy design.