The issue of neonatal mortality prediction continues to be a thorn in the flesh of healthcare since the numerous interconnected factors of clinical, demographic, and imaging-related variables have an effect on infant outcomes. Risk assessment is a critical issue that requires timely intervention through early and accurate risk assessment especially in the neonatal intensive care unit where decisions have to be made in times of uncertainty. In the current study, it is suggested to use an EfficientNet-based multimodal fusion model combined with an artificial intelligence-based clinical model of predicting risks and mortality in neonatal disease prevention. The framework integrates profound visual representations of medical images of neonatal cases with the help of EfficientNet and represented clinical data (birth weight, gestational age, APGAR scores, and laboratory measurements). The use of a feature fusion approach can combine heterogeneous data sources to allow the model to model both spatial patterns as imaged and contextual relationships as available in tabular clinical attributes. Before the model training, the data are preprocessed through normalization, missing values, and features selection to enhance the quality of data and robustness of the model. The merged feature representation is then classified in a predictive model based on AI which is optimized to perform optimally. The experimental analysis proves that the suggested multimodal model with an experimental evaluation shows a better predictive ability than one-modality models based on the effective utilization of complementary information. The findings suggest that EfficientNet combined with clinical data modeling is a reliable and scalable decision-support model of neonatal outcome prediction in clinical practice.