Early muscular calf vein thrombosis (MCVT) detected within 48 h after injury may affect perioperative management in patients with hip fractures. This study aimed to develop and internally validate a nomogram for estimating early MCVT risk in this population. This study retrospectively screened hip fracture patients admitted between 2021 and 2024. Patients were categorized into MCVT and non-MCVT groups based on an admission ultrasound performed within 48 h after injury. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection, and restricted cubic splines (RCS) were used to evaluate nonlinear associations. A multivariable logistic regression model was constructed and visualized as a nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration (Brier score), and clinical utility (decision curve analysis, DCA), with internal validation using 1,000 bootstrap resamples, following Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Plus Artificial Intelligence (TRIPOD + AI) guidance. Among 665 patients included, 44 (6.6%) had early MCVT. Six variables were retained in the final prediction model: time from injury to admission (TFIA), triglycerides (TG), total cholesterol (TC), activated partial thromboplastin time (APTT), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). RCS analysis revealed a significant nonlinear association between TG and MCVT risk (p-value for non-linearity <0.001). The model showed good discrimination (AUC: 0.894; 95% CI: 0.829–0.941) and acceptable calibration (Brier score: 0.041). The optimism-corrected C-index was 0.884. DCA suggested potential clinical net benefit across relevant threshold probabilities. In conclusion, the developed nomogram may assist early risk stratification, but external validation in independent cohorts is required before clinical application.