Background <p>To address the lack of dynamic prognostic tools for the rare Giant Cell Glioblastoma (gcGBM), this study utilized conditional survival (CS) analysis and a population-based nomogram to provide evolving, personalized survival estimates beyond baseline diagnosis.</p> Methods <p>We identified 490 patients with gcGBM from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2022). The cohort was split into training (<i>n</i> = 343) and validation (<i>n</i> = 147) sets. Conditional survival probabilities—CS(t|s) = S(t + s)/S(s)—were calculated to assess the likelihood of surviving an additional t years given s years already survived. A Random Survival Forest with Recursive Feature Elimination (RSF-RFE) algorithm was employed to select the most parsimonious predictors. A CS-nomogram was constructed and evaluated using calibration curves, time-dependent AUC, and Decision Curve Analysis (DCA). An interactive web-based calculator was also developed.</p> Results <p>The baseline 5-year overall survival (OS) was 14%. However, CS analysis revealed that the prognosis improved significantly over time: for patients who survived 3 years, the probability of surviving to year 5 rose to 69%, and reached 83% for those surviving to year 4. The RSF-RFE identified six key predictors: age, sex, tumor site, surgery, radiotherapy, and chemotherapy. The resulting CS-nomogram demonstrated high discriminatory power and clinical net benefit in both cohorts. Risk stratification effectively distinguished high-risk and low-risk groups.</p> Conclusion <p>Prognosis in gcGBM is a dynamic process. The risk of mortality is highest in the early years post-diagnosis but decreases substantially for long-term survivors. Our validated CS-nomogram and web-based tool provide clinicians with a practical means to offer personalized, time-adjusted survival estimates, facilitating better patient counseling and refined follow-up strategies.</p>