Suicide is an act aimed at intentionally ending one’s life. Suicide cases are a serious issue that remains prevalent in Indonesia, particularly in Central Java, which has experienced an increasing trend in recent years. This study aims to analyze the patterns of suicide cases using the K-Means Clustering algorithm based on attributes such as age range, suicide method, and incident location. The CRISP-DM approach is applied in the data processing stages of this research. The study utilizes data from 103 cases obtained from the Semarang and Boyolali District Police Departments. The analysis process produces three risk categories: High Risk, Medium Risk, and Low Risk, which are visualized using Principal Component Analysis (PCA). The model evaluation uses the Elbow method and the Silhouette Score, indicating adequate clustering results. The silhouette score obtained from this study is 38%, which suggests that the quality of separation between clusters is quite good. Based on the clustering results, the high-risk group is dominated by elderly individuals hanging in open areas. This study provides critical insights into the distribution patterns of suicide cases in the research area. This information can be utilized to formulate more effective and targeted prevention strategies to reduce suicide rates in the future.