This study examines sectoral segmentation and classification in emerging economies using a multivariate analysis framework. The study focuses on selected economic indicators, including gross domestic product (GDP), transport and communication, education, agriculture and natural resources, health, investment, government expenditure, employment, and tourism. Factor Analysis (FA), Principal Component Analysis (PCA), K-Means clustering, Linear Discriminant Analysis (LDA), and correlation analysis were employed to identify underlying dimensions, segment observations according to economic characteristics, and evaluate the distinctiveness of the resulting groups. The results indicate that the data are suitable for factor analysis, with a Kaiser-Meyer-Olkin value of 0.620 and a statistically significant Bartlett's Test of Sphericity (χ² = 130.674, p < 0.001). Factor analysis identifies a dominant economic development dimension strongly associated with GDP, investment, government expenditure, and transport. Similarly, the first three principal components explain 87.75% of the total variance, with the first component accounting for 40.89%. K-Means clustering produces three distinct groups representing relatively high-, moderate-, and low-performing economic profiles. Linear Discriminant Analysis achieves an overall classification accuracy of 86%, indicating meaningful separation among the identified groups. Furthermore, employment and tourism exhibit a moderate positive relationship (r = 0.54). The findings demonstrate that multivariate techniques provide complementary insights into the structural heterogeneity of economic sectors in emerging economies. However, given the relatively small number of observations, the findings should be interpreted as exploratory. Future studies should validate the identified segmentation using larger datasets, alternative clustering criteria, and independent or cross-validated classification procedures.