This study used a novel mixed-method approach which integrates quantitative analysis and geospatial statistics with artificial intelligence (AI) techniques to assess U5MR in the Middle Euphrates provinces of Iraq in 2022. We examined spatial patterns, socioeconomic determinants and health system associated with child mortality, Based on the data of the Iraqi Ministry of Health for 2022. We revealed broad regional discrepancies, with the maximum mortality rate in Al Diwaniyah province 33.1 per 1000 and fell in al-Muthanna province to 16.6 per 1000 live births. Detailed statistical analysis highlighted key drivers of this variability, namely rural population density 15% increased risk, maternal levels of education 10% reduced risk and accessibility to health services 40% reduced risk). The three most prevalent causes of death identified were respiratory infections 30%, malnutrition 25%, and diarrheal diseases 20%. Provincial differences in mortality rates are explained 65% by the availability of health care services according to the study. The other particularly critical area was postnatal care visits, with each additional visit linked to a 5% decrease in the chances of dying. By leveraging artificial intelligence techniques via TensorFlow, we built predictive models that reached 85% accuracy in identifying areas at high risk based on socioeconomic and health indicators. A geospatial analysis using ArcGIS revealed the sharp spatial clustering of mortality rates, closely linked with the distribution of healthcare infrastructure. Thus, providing evidence-based implications for policymakers and clinicians in the region indicating that mortality could be reduced by enhancing accessibility of health services, better education of mothers and availability of some preventive measures.