This approach makes use of cutting-edge machine learning techniques to improve early diagnosis, better treatment protocols, and supply neonatal intensive care units (NICUs) with continuous monitoring capabilities. The following are the essential parts that make up this whole. Collecting the Data and Performing Preprocessing: We collect clinical data from a wide variety of sources, some of which include electronic health records (EHRs), medical sensors, laboratory findings, and imaging tests. The data are preprocessed to get rid of any outliers, fill in any gaps in the data, and make sure the data are consistent. First, we use an algorithm called Random Forest (RF) for early diagnosis. This allows us to determine the chance of newborn problems, such as sepsis or respiratory distress syndrome, occurring. The results of the separate decision trees are combined in RF before any predictions are made, which guarantees a reliable and accurate early diagnosis. Real-time monitoring of newborn vital signs and clinical data is also a component of our system. Anomaly detection algorithms are used in this process to discover deviations from the normal range of values. This individualized approach to newborn care ensures that appropriate therapies are performed at the appropriate times, which leads to improved patient outcomes. To ensure that a human-centered approach is maintained in the field of healthcare, the technique incorporates many ethical precautions, such as protections for patients' privacy and openness.