Abstract This research adopted a novel mixed-method approach combining quantitative analysis and geospatial statistics with artificial intelligence (AI) techniques to evaluate U5MR in the Middle Euphrates provinces of Iraq in 2022. We investigated spatial patterns, socioeconomic determinants and health system factors related to child mortality, Using data from the Iraqi Ministry of Health in 2022. We uncovered significant disparities across the regions, where the highest death rate in Al Diwaniyah province was 33.1 per 1000 and dropped to 16.6 per 1000 live births in al-Muthanna province. An exhaustive statistical analysis brought to the fore the main factors that accounted for such a variation, that is to say population density in rural areas was responsible for a 15% increased risk of maternal levels of education for a 10% reduced risk and Access to health services for a 40% reduced risk). The leading 3 causes of death were first of all respiratory infections (30%), then malnutrition (25%), and finally diarrheal diseases (20%). Most of the differences in death rates at the provincial level are attributed to the availability of health care services, i.e., 65%, in accordance with the study. The other factor that was equally critical, if not more so, was postnatal care visits. Every additional visit was associated with a 5% reduction in the probability of death. We developed predictive models that reached 85% accuracy in pinpointing areas at high risk on the basis of socioeconomic and health indicators by means of artificial intelligence techniques through TensorFlow. Geospatial analysis was conducted with ArcGIS to identify the acute spatial clustering of mortality rates that were in close connection with the distribution of healthcare infrastructure. Thus, providing evidence-based implications for policymakers and clinicians in the region indicates that mortality could be reduced by enhancing accessibility of health services, better education of mothers and availability of some preventive measures.