Due to their ability to model temporary dependencies in sequential data, recurrent neural networks (RNNs) have received a lot of recognition in the healthcare industry. This makes them ideal for venture patient outcomes. The use of RNNs in healthcare to predict patient outcomes like disease progression, treatment responses, etc., is the focus of this study. The proposed RNN-based models aim to provide meticulous and timely predictions that can assist healthcare professionals in making decisions by utilizing longitudinal patient data, such as lab results, treatment records, electronic health records (EHRs), and so on. In order to improve predictive performance, the research emphasizes the significance of feature selection, model architecture, and data preprocessing. Relative examinations with conventional AI models show the practicality of RNNs in catching complex worldly examples, at last adding to the work on quiet consideration and asset portion. The results suggest that RNNs have a lot of potency for improving healthcare delivery and advancing personalized medicine.