Maternal mortality risk in Bangladesh remains a critical public health challenge, compounded by rural access gaps and the absence of scalable, data-driven early-warning systems. This study presents a reproducible, interpretable machine learning framework for maternal health risk classification using an IoT-collected dataset of 1,014 patient records and six physiological indicators; a deduplication audit identified 562 repeated sensor readings, a finding which is documented in the exploratory analysis. A rigorous pipeline was implemented encompassing five clinically grounded engineered features - Mean Arterial Pressure, Shock Index, Pulse Pressure, BP Ratio, and Composite Risk Score - alongsi... More >