Even with significant improvements in maternal health metrics, Bangladesh still faces challenges in ensuring safe childbirth. In the context of Bangladesh, this research explores the feasibility of applying machine learning (ML) techniques to identify and understand the complex interactions among variables that affect the safety of motherhood. Data from 14,062 women of reproductive age were included in the Demographic and Health Survey (DHS), which was the source of the data used in this study. A forward stepwise logistic regression approach was used to achieve the analytical goals. The odds ratios (OR) associated with safe motherhood showed significant increases for women who gave birth outside of the home (OR = 1.2), had antenatal care (ANC) (OR = 3.6), had a cesarean delivery (OR = 1.5), and lived in the Chittagong division (OR = 1.3). This study demonstrates how machine learning may be used to identify the complex variables that affect safe motherhood in Bangladesh. ML has the potential to guide targeted interventions and move Bangladesh one step closer to achieving SDG 3 by enabling data-driven insights.