Background: Dropping out of school is described as leaving school without completing a minimum requirement, whereas early marriage is marriage before the age of 18. Exploring the spatial distribution and identifying associated factors of these outcomes is important to designing programs to reduce early marriage and school dropout. Thus, this study aimed to identify factors affecting early marriage and school dropout in Ethiopia using bivariate binary multilevel analysis with spatial effect from EDHS 2016 data. Method: In this study, a total weighted sample of 11,647 and 2,939 married and school-enrolled women were used respectively from 2016 EDHS data for the analysis. The study was evaluated by spatial, separate, and bivariate binary classical and multilevel analysis models. ArcGIS version 10.8 and SAS version 9.4 statistical software were used for spatial and logistic methods respectively. Results: The prevalence of early marriage and school dropout in this study was 62.9% and 75.4% respectively. This study also revealed that the spatial distribution of early marriage and school dropout was nonrandom in Ethiopia with Moran’s index statistics 0.2958(P-value<0.00001) and 0.0949 (p-value<0.00001) respectively. The dependency parameter estimation of bivariate binary multilevel was differing from unity (1.393), which reflects the statistical significant dependency between the two outcomes. Conclusions: This study demonstrated that both early marriage and school dropout after married vary across clusters in Ethiopia. Based on AIC, and BIC, a bivariate binary multilevel model was preferred. Women who lived in rural areas, whose decision to marry was decided by their parents, and women who have a poor wealth index were more likely to be exposed to early married and school dropouts. Therefore, it needs great interventions on these indicators to minimize early marriage and school dropout after marriage. Keywords: early marriage, school dropout, spatial analysis, bivariate binary multilevel