Purpose: To investigate whether widely available lumbar spine radiography can predict lumbar spinal canal stenosis (LSCS). Methods: This retrospective, single-center cross-sectional study included patients who underwent both lumbar spine radiography and magnetic resonance imaging. Correlations between radiographic and MRI parameters and their associations with LSCS were analyzed with adjustment for age and sex. The predictive performance of machine learning algorithms was evaluated with nested cross-validation. Results: Among 201 subjects, 104 had L4/5 LSCS and 97 did not; the LSCS group was 18 years older. After adjustment for age and sex, static spondylolisthesis (aOR 5.62, p < 0.001), average disc height (aOR 1.23, p = 0.021), and dynamic lordotic angle (aOR 0.96, p = 0.011) remained significant predictors, and adding radiographic parameters to age and sex increased the AUC from 0.85 to 0.91 (p = 0.007). Static spondylolisthesis remained significant in an age- and sex-matched subset (aOR 4.81). In nested cross-validation, machine learning models combining radiographic parameters with age and sex achieved an accuracy of 80.7% (sensitivity 82.9%, specificity 78.4%, AUC 0.87), compared with 76.2% for radiographic parameters alone. Conclusions: Lumbar spine radiography provides information on LSCS that is additional to age. Radiographic signs reflecting the stabilization phase of spinal degeneration, particularly static spondylolisthesis and decreased dynamic lordosis, were the most robust predictors. External validation is required before clinical use.