Purpose: The main purpose of this study was to develop a low-cost integrated medical device. This device will help investigate the risk levels of pregnant patients and reduce the cost of medical diagnosis for poor countries such as Bangladesh, where maternal healthcare is a great concern. Research Methodology: A device equipped with multiple sensors was developed to collect raw data from pregnant patients. This data is transmitted to the cloud, where open-source algorithms process and analyze it to identify patient risk levels. Results: We developed the system, collected raw data from patients, and uploaded these data to our cloud system. The data were processed in the cloud, and the resultant data were presented in the form of graphs. From these graphs, the risk levels were determined. Conclusion: The IoT-based integrated device showed approximately 93% accuracy compared with conventional methods. It is a cost-effective, scalable, and adaptable solution that is suitable for maternal healthcare in developing countries. Features such as plug-and-play sensors, real-time cloud processing, and machine learning-based diagnostics make it a promising innovation for reducing maternal and infant mortality rates. Limitations: The device is designed solely for use in pregnant patients and requires authorization from health regulators. Some high-cost sensors were excluded to ensure affordability.. Contribution: The main contribution of this study is to minimize the costs involved in maternal healthcare in poor countries such as Bangladesh. This, in turn, controls the death of mothers and children by improving maternal healthcare facilities.