Introduction: Improving maternal healthcare is essential for safeguarding the well-being of expectant mothers during pregnancy and childbirth. Despite significant progress, challenges such as infections, hemorrhage, hypertension, unsafe abortions, and other issues persist. Prioritizing maternal health can mitigate mortality and promote safe pregnancies. Aim: This systematic review assesses research methodologies in maternal healthcare outcomes, evaluating their strengths and weaknesses. We also explore the prevalence of systematic reviews in maternal health to enhance healthcare outcomes. Methods: We examined five major databases—Google Scholar, PubMed, Elsevier, PLOS, and BMC, encompassing descriptive and computational research on maternal outcomes, between 2000 and 2021. Our search terms included Predicting, Modeling, Maternal, Outcome, Healthcare Forecasting, Demonstrating, Motherly, Consequence, Diagnosis, Machine Learning, Mathematical, and Statistical. Results: We reviewed 44 papers related to maternal outcomes. Google Scholar yielded 50 articles (46.30%), PubMed 33 articles (31.48%), Elsevier 12 articles (11.11%), BMC nine articles (9.26%), and PLOS two articles (1.85%). Conclusion: Our findings highlight the high awareness of maternal outcome prevalence. Multiple factors contribute to maternal risk, including maternal education, economic circumstances, financial constraints, and access to antenatal care. Therefore, this work advocates for the adoption of additional methods and mathematical models to predict maternal outcomes, ultimately improving maternal healthcare.

Keywords: Descriptive statistics, Computational models, Maternal outcomes, Developed and Less-developed countries.