Analysis of 115 spinal cord injury patients reveals neurogenic bladder affects 30.43% of cases, with age, ASIA injury grade, C-reactive protein levels, and urination patterns serving as primary predictive factors. The multivariate model achieved high accuracy in identifying at-risk patients through bootstrap validation and ROC curve analysis. This predictive framework addresses a critical gap in spinal cord rehabilitation, where neurogenic bladder represents one of the most devastating secondary complications affecting long-term quality of life. Unlike urinary tract infections, which have been extensively studied in SCI populations, systematic risk stratification for neurogenic bladder has remained underdeveloped despite its profound impact on social functioning and independence. The identification of inflammatory markers like CRP as predictive variables suggests systemic inflammation may contribute to bladder dysfunction beyond direct neurological damage. For clinicians managing acute SCI cases, this model enables proactive intervention strategies rather than reactive treatment approaches. However, the relatively small cohort size and single-center design limit generalizability across diverse SCI populations. The validation approach using bootstrap resampling strengthens confidence in the model's reliability, though external validation across multiple trauma centers would enhance clinical utility.