Infants and toddlers with mild traumatic brain injury (mTBI) and minor subdural hematoma (SDH) were found to have a higher risk of requiring neurosurgical intervention (NI). However, the ability to identify patients with mTBI and minor SDH who require NI remains limited. This study aims to develop a nomogram to predict NI in these patients. A nomogram predicting NI was established using demographic, clinical, radiographic, and laboratory data from patients with mTBI and minor SDH. The least absolute shrinkage and selection operator (LASSO) regression and best subsets regression (BSR) methods were employed to identify variables and select predictive factors. A nomogram was constructed using multivariable logistic regression. The model's performance was evaluated using the area under the receiver operating characteristic curve, calibration curves, the Hosmer-Lemeshow test, and decision curve analysis. Immediate seizures, anemia, and subarachnoid space depth were identified as significant predictive factors by the BSR, leading to the development of a nomogram. The AUC for this nomogram, obtained through bootstrap validation (resampling = 500), was 0.893 (95% CI, 0.844-0.942). The model demonstrated good calibration, and decision curve analysis showed that when the threshold probability ranged from 7 to 83%, using the nomogram to predict NI provided a net benefit. A novel nomogram has been developed to accurately assess the risk of NI in children under 3 years of age with mTBI and minor SDH, potentially aiding in clinical decision-making.