This study aims to identify key factors influencing hospitalization costs for elderly patients undergoing laparoscopic surgery and to provide theoretical support for the refined management of hospital expenses through a grouping model. A retrospective analysis was conducted on the medical records of 1,010 elderly patients who underwent laparoscopic surgery between 2018 and 2023 at a specific hospital. Descriptive statistical analysis, univariate analysis, linear regression, and a regression decision tree model were employed to thoroughly examine the factors affecting hospitalization costs. The decision tree model was further used to categorize patients into distinct groups. The primary factors identified as influencing hospitalization costs were age, length of hospital stay, number of comorbidities, disease outcomes, preoperative hospital stay, and surgical site. Using the decision tree model, with age, number of comorbidities, and preoperative hospital stay as key indicators, patients were effectively categorized. Healthcare institutions can focus on these modifiable factors for targeted treatment and care. Applying the classification standards derived from the decision tree model to standardize diagnostic and treatment processes can assist in managing hospitalization expenses, thereby reducing the economic burden on patients and serving as a reference for healthcare payment system reforms.