The integration of Artificial Intelligence (AI) in healthcare has the potential to significantly enhance the accuracy of patient outcome predictions. This study explores the application of machine learning techniques to predict patient outcomes using data collected from hospital records. A survey-based methodology was employed, analyzing a sample of 500 patient records, including demographic information, medical history, treatment details, and outcomes. Several machine learning models, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks, were trained and evaluated. The Random Forest model achieved the highest accuracy, followed by Neural Networks and SVM. The results demonstrate that machine learning models can effectively predict patient outcomes, highlighting their potential to improve clinical decision-making and patient care. The study underscores the importance of high-quality data and the need for further research to refine these models for specific clinical applications. By leveraging these models, healthcare providers can enhance clinical decision-making, improve patient outcomes, and optimize resource management. This research contributes to the growing body of evidence supporting the use of AI in healthcare, paving the way for more sophisticated and integrated AI solutions in clinical practice.