The healthcare sector has seen remarkable progress with the integration of Radio Frequency Identification (RFID) technology and sensors to monitor infants and mitigate mortality rates. By combining RFID technology with sensors, healthcare facilities gain access to a range of applications that streamline workflows, enhance patient care, and improve overall efficiency. Furthermore, when augmented with Machine Learning (ML), this synergy opens avenues for data-driven insights, predictive analytics, and finely-tailored healthcare management. In India, neonatal deaths remain a significant concern, particularly within the first week of life and alarmingly within the initial 24 h, urgent measures are needed to address this issue. To tackle neonatal mortality effectively, our proposed solution involves deploying an interpretable machine learning framework integrated with an RFID-based hospital monitoring system. This integrated system allows for real-time analysis, enabling early detection and intervention, ultimately leading to a reduction in neonatal mortality rates. Through the collection of real-time data on pregnant women's vital signs utilizing RFID technology and sensors, coupled with an interpretable ML ensemble classifier, healthcare providers can swiftly identify at-risk newborns, facilitating timely intervention and potentially saving numerous lives. We conducted an analysis on 40,000 infant records from the National Center for Health Statistics (NCHS) using logistic regression, aiming to predict mortality risk based on 30 attributes. While this approach yielded good precision (0.83) for survival prediction, it struggled to accurately identify non-survivors, prompting the need for further investigation. While ensemble models like BaggingClassifier and RandomForest classifier achieved high accuracy rates, the Bagging Classifier with Logistic Regression demonstrated outstanding performance in determining whether a newborn is alive at the time of report. Its remarkable performance in terms of accuracy (99.68%), precision (99.68%), recall (100%) and F1 score (99.84%) highlights how effective it was for this classification job. Finally, the SHapley Additive exPlanations (SHAP) analysis is employed to interpret the correlation between the risk features and the outcome.