The sudden onset of acute pancreatitis presents significant challenges in clinical management. Early identification of this disease is crucial for timely intervention and improved patient outcomes. Machine learning models have emerged as promising tools for predicting acute pancreatitis, utilizing diverse data sources and algorithms. This systematic review aims to comprehensively explore the landscape of machine learning predictive models for acute pancreatitis. We delve into the significance of early identification, the diverse methodologies employed, and their clinical utility. Our rigorous methodology includes a comprehensive search strategy, inclusion and exclusion criteria, data extraction and analysis, and systematic evaluation of the selected studies. The systematic review provides insights into feature selection and engineering, data sources, model types and algorithms, and performance evaluation metrics. It also offers a detailed review of study characteristics, data sources, feature importance, model performance, and clinical utility. The discussion section emphasizes key findings, limitations, and future research directions. The review concludes with a summary of the state of the field and implications for clinical practice, highlighting the potential for early prediction models to transform patient care. This review serves as a comprehensive resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and acute pancreatitis