It is crucial to be aware of the potential hazards involved in exchange transfusion (ET), even though it has the potential to save lives in severe cases of neonatal hyperbilirubinemia. The investigators reviewed the medical histories of neonates who were diagnosed with hyperbilirubinemia and treated with ET in a children’s hospital within the first 30 days of birth between the years 2015 and 2020. Traditional statistical methods and cutting-edge, explainable artificial intelligence (XAI) were utilized to determine the risk factors. The study included 188 cases of ET and found that hyperglycemia was present in (86.2%) of patients, that (50.5%) of patients required additional transfusions after ET, that (42.6%) of patients had hypocalcemia, (42.6%) had hyponatremia, (38.3%) had thrombocytopenia, (25.5%) had metabolic acidosis (25.5%), and 25.5% had hypokalemia. XAI made some fascinating discoveries and found some startling findings. XAI has given medical professionals the ability to better analyze nonlinear interactions and generate actionable knowledge for treatment, in addition to improving their efficacy in forecasting bad outcomes that may occur during ET. This has been made possible by the fact that XAI has provided these professionals with the ability to better analyze nonlinear interactions. XAI has also enabled medical professionals to better predict adverse events that may occur during ET.