Community welfare is a primary objective of national development, encompassing various aspects such as health, education, and decent employment, all of which play crucial roles in achieving national stability and progress. However, welfare is not solely dependent on economic factors but also on the overall quality of life. Unfortunately, disparities in welfare persist across different regions, influenced by local environmental factors, including access to education, which in turn affects job opportunities and income levels. Inequalities in employment opportunities can potentially slow down national development by reducing the number of individuals capable of contributing productively to key economic sectors. To enhance national development, further analysis of welfare indicators such as the open unemployment rate, human development index, labor force participation rate, and poverty levels is essential. Therefore, this study conducts cluster analysis on welfare indicators across districts/cities in Central Java for the year 2023. Both hierarchical and non-hierarchical (K-Means) clustering methods are employed to identify patterns of inequality by partitioning data into groups based on specific similarities. This approach facilitates a more effective review of policies to address welfare disparities across various regions. The findings indicate that the welfare indicators in Central Java are in a relatively poor condition, with low labor force participation rates, low human development indices, and high poverty rates. The hierarchical and non-hierarchical cluster analysis identified 5 optimal clusters, with all welfare variables having significant influence, requiring four iterations to reach the final centroids.