This research harnesses the capabilities of explainable artificial intelligence (XAI) techniques, including Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, and Multi-Layer Perceptron, to advance maternal outcome predictions during pregnancy, with a specific focus on critical determinants such as age, pack cell volume, weight, and maternal blood pressure. Ethical clearance was obtained for comprehensive data collection from 2,000 patients in the Niger Delta region of Nigeria spanning 2019 to 2022 across various healthcare facilities. While achieving commendable predictive accuracy and providing valuable insights into maternal risk factors and personalized care interventions, this study acknowledges its limitations, notably dataset specificity and variable selection, emphasizing further research with diverse datasets and contextual settings to ensure robust validation. Through the introduction of XAI, this investigation aims to empower healthcare providers with a powerful tool for more precise maternal health risk assessment, streamlined data analysis, and enhanced treatment planning, ultimately saving valuable time and resources addressing a pivotal aspect of women's well-being during their childbearing years.

Keywords: Maternal outcomes, Explainable artificial intelligence (XAI), Machine learning algorithms, Risk factors and Maternal health