Infections during the neonatal period are one of the most critical factors leading to mortality in neonates in the neonatal intensive care unit (NICU) within the first 28 days of life. The majority of hospitalized neonates in NICU are premature and highly susceptible to nosocomial infections due to their compromised immune systems. Therefore, the objective of this research is to develop a model to predict neonatal infections, aiding in the early detection and management of infections among vulnerable neonates. The study involves neonates hospitalized in the NICU, with data collected from 113, 378 neonates admitted in the year 2022. Initial features for creating predictive models of neonatal infections were obtained by examining relevant sources of information and consulting with physicians and relevant specialists. In this research, data mining classification algorithms were used to create predictive models for neonatal infections. To evaluate the created models, the Recall, Accuracy, Precision and F1-Score indicators were utilized. Among the methods used, the Random Forest algorithm demonstrated the best performance in predicting neonatal infections. Among the four methods employed for balancing the data, the folding method notably improved the performance of models. Additionally, using a dataset that includes only maternal features can significantly contribute to predicting neonatal infections before the infant's birth.