Background: Osteosarcoma is the most common primary malignant bone tumor. However, research on predicting the prognosis of patients with osteosarcoma after chemotherapy (POC) remains limited. Notably, the Lung Immune Prognostic Index (LIPI) has emerged as a novel and effective prognostic factor in lung cancer. Therefore, this study aims to explore the prognostic significance of LIPI in POC for the first time, providing new insights and a foundation for evaluating the prognosis of these patients. Methods: This retrospective study analyzed patients with POC who were admitted to our center between January 2012 and January 2022. Hematological and clinical characteristics were collected and systematically evaluated. Kaplan-Meier survival analysis and Cox regression models were employed to assess the associations between various prognostic factors and overall survival (OS). Independent risk factors influencing OS were identified through both univariate and multivariate analyses. Based on these findings, a LIPI nomogram model was developed to predict OS in patients with POC. Results: This study included 150 patients who underwent chemotherapy, with 41 (27%), 80 (53%), and 29 (19.3%) classified into poor, moderate, and good prognostic categories, respectively, based on the LIPI classification (P < 0.0001). Time-dependent receiver operating characteristic (ROC) curve analysis demonstrated that LIPI exhibited superior prognostic predictive capability compared to other hematological and clinical parameters. Univariate and multivariate analyses identified LIPI as an independent prognostic factor. A nomogram was subsequently developed by integrating significant prognostic variables. Calibration curves confirmed the nomogram's accuracy in predicting three- and five-year overall survival (OS) post-chemotherapy. Furthermore, decision curve analysis indicated that the LIPI-based nomogram would provide substantial clinical benefits for chemotherapy patients. Conclusion: This study assessed the prognostic efficacy of LIPI in patients with POC and developed a LIPI-based nomogram to assist clinicians in predicting three- and five-year overall survival (OS). The proposed model has the potential to facilitate timely interventions and guide personalized management strategies, thereby improving patient outcomes.