his project explores the hidden structures within global health data by focusing on 46 Asian countries. Using the WHO Life Expectancy dataset, I applied various unsupervised learning techniques to group countries based on metrics like life expectancy, schooling, and adult mortality. Key features of this report include: Data Selection: Analysis focused on the year 2014 to ensure data completeness for critical variables like Alcohol consumption and Schooling. Methodology: A comparison of partitioning methods (K-means and PAM) and Hierarchical Clustering (Ward's method) to identify stable country groupings. Advanced Visualization: Use of dimensionality reduction techniques—MDS, t-SNE, and UMAP—to project complex, multi-dimensional data into intuitive 2D maps. Findings: The results reveal three distinct clusters: a "High-Performing" group (e.g., Japan, Singapore), an "Emerging" middle-income group (e.g., China, Thailand), and a "Challenged" group (e.g., Afghanistan, Yemen).