This study applies Automated Machine Learning (AutoML) and Explainable Artificial Intelligence (XAI) techniques to identify important factors influencing individual well-being. Using data from the “Human Information Database” and a well-being survey, a CatBoost regression model was selected as the best model. SHapley Additive Explanations (SHAP) and Individual Conditional Expectation (ICE) were then used to analyze the impact of various factors on well-being, both overall and at a type-specific level. Additionally, SHAP-based clustering was performed to group individuals with similar well-being drivers. A marketing-focused individual-level analysis was also conducted. These results provide actionable insights for developing personalized strategies to enhance well-being, offering practical applications through data-driven analysis.