Stunting is a serious public health issue that poses a long-term threat to the quality of human resources. North Aceh Regency is one of the regions with a relatively high prevalence of stunting, requiring targeted and effective intervention strategies. This study aims to classify regions based on their level of stunting vulnerability to support data-driven decision-making. The Fuzzy C-Means (FCM) clustering algorithm was selected due to its ability to handle data with flexible membership degrees, making it suitable for complex classification tasks. The data used in this research were obtained from the North Aceh Health Office for the year 2023 and include variables such as the number of children recorded in the E-PPGBM system, newly entered children in 2023, and the percentages of stunting, wasting, and underweight across 32 subdistricts. The research process involved data collection, literature review, system design and implementation using the Python programming language, and analysis of clustering results. The findings reveal that the 32 subdistricts can be grouped into three main clusters: high vulnerability (13 subdistricts), medium vulnerability (6 subdistricts), and low vulnerability (13 subdistricts). These clusters facilitate the visualization and identification of priority areas requiring more focused stunting interventions. In conclusion, the FCM algorithm proved effective in clustering regions based on stunting-related data. The implication of this study is to provide a foundation for local governments in formulating more efficient and targeted stunting intervention strategies according to the vulnerability level of each area.