This project focuses on the comprehensive data validation and analysis of the MOVER dataset, a repository capturing hospital visits for surgery patients at the University of California, Irvine Medical Center. The initial focus is on conducting data validation procedures to ensure the dataset's reliability. Followed by conducting in-depth exploratory data analysis to extract meaningful insights and trends, constructing machine learning models to guide hospitals throughout the patient's treatment journey, from pre-surgery to post-surgery procedures. Additionally, the project aims to develop models facilitating efficient hospital resource management, encompassing beds, utilities, and other critical elements. Addressing these objectives, the project seeks to make a significant contribution to healthcare optimization by providing insights derived from data analysis and ML models.