This article explores the application of clustering analysis to model the attractiveness of Moroccan provinces. By employing the K-means clustering algorithm, we identified three distinct clusters based on key socio-economic indicators from the 2014 census, such as employment rates, housing conditions, and educational attainment. Each cluster presents unique characteristics, providing valuable insights into the varying levels of provincial attractiveness. The analysis highlights significant disparities and potential areas for policy intervention. The findings suggest tailored strategies for regional development, aiming to enhance the livability and economic prospects of the provinces. This study underscores the importance of data-driven approaches in informing policy-making and strategic planning, ultimately contributing to balanced and sustainable regional growth.