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Introduction Postnatal care (PNC) is recommended for the optimal health of mothers and newborns; however, PNC uptake remains poor in sub-Saharan Africa. Traditional statistical approaches have been used to predict healthcare service utilisation more often but are limited in examining complex relationships compared with decision tree (DT) models.Objectives This study aims to predict the main risk factors of PNC non-utilisation in three neighbouring East African countries using the DT models.Methods PNC non-utilisation meant that both the mother and neonate did not receive at least one postnatal check within 6 weeks after delivery. Demographic and Health Surveys data from the Democratic Republic of Congo (DRC) (2013/2014), Kenya (2014) and Tanzania (2015/2016) were used. The DT model’s predictive performance was compared with the standard logistic regression (LR) using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve.Results DT models exhibited higher accuracy and sensitivity than LR models. In the DRC, women with low-quality antenatal care (ANC), home deliveries and unemployment had the highest probability of PNC non-utilisation (92.0%). In Kenya, women who had home deliveries, unemployment and limited access to mass media showed the highest likelihood of PNC non-utilisation (87.0%). In Tanzania, women with home deliveries, low-quality ANC and unwanted pregnancies exhibited the highest likelihood of PNC non-utilisation (100.0%).Conclusion Women with low-quality ANC, home deliveries, unemployment, unwanted pregnancies and limited access to mass media were classified as high-risk groups of PNC non-utilisation. These findings can help prioritise interventions and enhance PNC uptake in East Africa. Additionally, DT models can be applied as valuable tools for predicting maternal and child healthcare services utilisation in other sub-Saharan African countries.