Objective The objective of this study was to investigate the predictive value of inflammatory and nutritional factors in PD-1 inhibitor treatment in patients with advanced non-small cell lung cancer (NSCLC).Methods The complete medical records of 70 patients with advanced NSCLC treated with PD-1 inhibitor immunotherapy in the First Affiliated Hospital of Anhui University of Science and Technology from July 2020 to November 2023 were retrospectively analyzed. Survival analysis was performed by Kaplan-Meier method, and univariate and multivariate Cox analysis was used to screen for independent risk factors affecting patients' PFS. RStudio was used to build the clinical prediction model, and C-index, ROC curve, calibration curve and DCA curve were used to evaluate the model, and bootstrap method was used for internal verification. Results There was no significant correlation between CRP, CAR, GPS and ORR (P>0.05). The DCR of low CRP and CAR was significantly increased (P=0.002, P=0.001), and DCR of patients with low GPS was significantly increased (P=0.005). Kaplan-Meier survival analysis showed that the median PFS (mPFS) of L-CRP group was 14.77 months, which was significantly higher than that of H-CRP group (P=0.002). The mPFS of L-CAR group was 24.53 months, which was significantly higher than that of H-CAR group which was 5.63 months (P<0.001). mPFS of 13.83 months in the GPS 0 group was significantly higher than 5.63 months in the GPS 1 group and 8.40 months in the GPS 2 group (P=0.041). Multivariate Cox analysis showed that CAR (P=0.001) was an independent prognostic factor for PFS in NSCLC patients treated with PD-1 inhibitors. CRP, CAR and GPS in this study were incorporated into the clinical prediction model by RStudio, and the model was tested to have good predictive value.Conclusion In this study, serum CRP, CAR and GPS were used to construct a clinical prediction model, which has good differentiation and calibration degree, and is of great value for predicting the anti-PD-1 treatment effect of middle and advanced NSCLC