Introduction Exclusive breastfeeding (EBF) is the safest and healthiest option of feeding among infants in the first 6 months throughout the world. Thus, the promotion of EBF is essential to prevent complex infant health problems even at the adulthood level. But the majority of previous studies focused on individual- level determinants of EBF by using basic regression models in localized areas. This study aimed to identify the determinants of EBF at the individual and community level which would be helpful to design appropriate strategies for improving the practice of EBF. Methods It is a secondary data analysis using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 1185 infants under 6 months of age were included in the analysis. A Multilevel logistic regression model was employed to investigate factors significantly associated with EBF among under-six infants in Ethiopia. Adjusted odds ratio (AOR) with 95% confidence interval (CI) was used to measure the association of variables whereas Intracluster correlation (ICC), median odds ratio (MOR), and proportional change in variance (PCV) were used to measure random effects (variation). Result In multilevel logistic regression; 4–5 months age infant (AOR = 0.04, 95%CI:0.02–0.07), female infants (AOR = 2.51, 95%CI:1.61–3.91), infant comorbidities (AOR = 0.35, 95%CI: 0.21–0.57), richest household wealth index (AOR = 10.34, 95%CI: 3.14–34.03) and antenatal care (AOR = 2.25, 95%CI:1.32–3.82) were individual- level determinants significantly associated with exclusive breastfeeding. Whereas, contextual region (AOR = 0.30, 95%CI: 0.10–0.87), community- level of postnatal visit (AOR = 2.77, 95%CI: 1.26–6.58) and community -level of maternal employment (AOR = 2.8, 95%CI: 1.21–6.47) were community level determinants significantly associated with EBF. The full model showed that46.8% of the variation of exclusive breastfeeding was explained by the combined factors at the individual and community levels. Similarly, it showed that the variation in exclusive breastfeeding across communities remained statistically significant (ICC = 8.77% and variance = 0.32 with P75% of women are utilizing ANC) [25]. The Community level of PNC utilization was the proportion of women within specific cluster who visit PNC some number of times. It was categorized as low (when≤50% of women utilized PNC) and high (when>50% of women utilized PNC) [25]. Community- level of media exposure was an aggregate respondent level of exposure for different types of media categorized as “50% = high media utilized communities” [25]. Community level of poverty was an aggregate wealth index categorized as “50% = Low poverty communities” [25]. Contextual region Ethiopia is demarcated for administrative purpose into 11 regions, which are classified as an agrarian, pastoralist, and city based according to the living status of the population. The regions Tigray, Amhara, Oromia, SNNP, Gambella, and Benshangul Gumuz were categorized as agrarian. Somali and Afar regions were grouped to form pastoralist region and Harari region, Addis Ababa and Dire Dawa city administrations were grouped to form city- based populations [14,25]. Community- level of women education was the proportion of women in the community who have primary or higher education, which was categorized as low (when≤25% of women were educated), middle (when 25–75% of women were educated), and high (when >75% of women were educated) [25]. Community level employment status was the proportion of women who were employed (had to work) in the specific cluster. It was categorized as low (when≤50% of mothers were employed) and high (when>50% of mothers were employed) [24,25]. Sample weight was done to compensate for the unequal probability of selection between the strata that were geographically defined, as well as for non-responses. Weighing of individual interview produces the proper representation of exclusive breastfeeding and related factors. Coding, recoding, and exploratory analysis was performed. Categorization was done for continuous variables using information from different works of literatures and re-categorization was done for categorical variables accordingly. For data analysis, STATA version 14.1 was used and descriptive statistics were used to present frequencies, with percentages in tables and using texts. Four models were considered in the multilevel analysis to determine the model that best fits the data; Model one (Null model) without explanatory variable was developed to evaluate the null hypothesis that there is no cluster level difference in exclusive breastfeeding practice that specified only the random intercept and it presented the total variance in exclusive breastfeeding practice among clusters. Model two adjusted for an individual variable which assumes a cluster level difference of EBF practice is zero. Model three to evaluate community level factors by aggregate cluster difference of exclusive breastfeeding practice. Model four included both adjusted individual and community level factors. The log of the probability of Exclusive breastfeeding was modeled using a two-level multilevel model as follows: [26,27]. Where, i and j are the level 1 (individual) and level 2 (community) units, respectively; X and Z refer to individual and community-level variables, respectively; πij is the probability of exclusive breastfeeding for the ith infant in the jth community; the β’s is the fixed coefficients. Whereas, β0 is the intercept-the effect on the probability of exclusive breastfeeding use in the absence of influence of predictors and uj showed the random effect (effect of the community on exclusive breastfeeding) for the jth community, and eij showed random errors at the individual levels. By assuming each community had a different intercept (β0) and fixed coefficient (β), the clustered data nature and the within and between community, variations were taken into account. Multilevel logistic regression analysis was used to analyze the data since it is appropriate for DHS data as it had a hierarchical nature. Multilevel modeling was providing unexplained variation in exclusive breastfeeding due to unobserved cluster factors called the random effect. All models included a random intercept at the cluster level to capture the heterogeneity among clusters. The measures of association (fixed-effects) estimate the association between likelihood of infants to exclusively breastfeeding as the AOR with 95% CI of various explanatory variables were expressed. Crude association between independent variables and the dependent variable was done independently and variables having p ≤0.2 in Bi-variable analysis were used to fit multivariable analysis model. At multivariable analysis,variables with p≤0.05 with confidence interval not including the null value (OR = 1)were considered as statistically significant variables with exclusive breastfeeding practice. The measures of variation (random-effects) were reported using Intra-cluster correlation (ICC), Median Odds Ratio (MOR), and Proportional Change in Variance (PCV). ICC was used to explain cluster variation while MOR is a measure of unexplained cluster heterogeneity [27]. The ICC shows the variation in exclusive breastfeeding of infants undersix months of age due to community characteristics. The higher the ICC (ICC>5%), the more relevant was the community characteristics for understanding individual variation in exclusive breastfeeding of infants. The ICC can be calculated as follows: [ICC=δ2uδ2u+δ2e] where δ2u = between group variation, δ2e = with in group variation OR [ICC=δ2δ2+π23], where δ2 is the estimated variance of clusters [26]. The STATA software command can also compute the ICC value of each model. MOR is defined as the median value of the odds ratio between the area at highest likelihood and the area at the lowest likelihood of exclusive breastfeeding when randomly picking out two areas and it measures the unexplained cluster heterogeneity; the variation between clusters by comparing two persons from two randomly chosen different clusters. MOR can be calculated using the formula [26]. In this study, MOR shows the extent to which the individual probability of being exclusively breastfed is determined by residential area. The proportional change in variance [PCV = (VA − VB)/VA) * 100] where VA = Variance of initial model and VB = Variance of model with more terms measures the total variation attributed by individual level and community level factors in the multilevel model [26]. PCV was computed for each model concerning the empty model as a reference to show power of the factors in the model explains exclusive breastfeeding practice. Log- likelihood test, Deviance Information Criteria (DIC), and Akaike Information Criteria (AIC) were used to estimate the goodness of fit of the adjusted final model in comparison to the preceding models (individual and community level models). Thus, the model with the highest value of Log likelihood test and with lowest values of DIC and AIC was considered to be the best fit model. Ethical clearance was obtained from the Ethical Review Committee of the College of Medicine and Health Sciences, Wollo University with approval and supporting letter. Permission to access the data set was obtained from the Measure DHS International Program. The data was only used for purpose of this study and not shared with a third party. All data used in this study were anonymous publicly available and aggregated secondary data with not having any personal identity. The data was fully available on the full DHS website (www.measuredhs.com).