Indonesia Emas 2045 is a government program with the vision of creating superior human resources and enhancing societal welfare, with one of its primary priorities being the reduction of social disparities. Identifying the level of societal welfare is crucial for formulating appropriate policy strategies. However, the welfare level in Indonesia remains uneven. This study aims to classify cities/regencies in Indonesia based on socio-economic aspects using the K-Means method combined with Principal Component Analysis (PCA) on five variables: Life Expectancy, Percentage of Economically Disadvantaged Groups, Average Years of Schooling, Expenditure per Capita, and GDP. The combination of K-Means and PCA is used to reduce thedimensionality of the research variables. The analysis results indicate three maincomponents, and the elbow method determines the optimal number of clusters to be two (2), with an SSE value of 1454.63. The first cluster (1) consists of 336 cities/regencies with a lower percentage of economically disadvantaged groups, while the second cluster (2) comprises 178 cities/regencies with lower Life Expectancy, Average Years of Schooling, Expenditure per Capita, and GDP. The clustering accuracy is assessed using the Davies-Bouldin Index (DBI), showing moderate clustering quality with a value of 1.019679.