Background <p>Th1 (IFN-γ⁺CD4⁺)/CD4⁺ cells exacerbate the release of pro-inflammatory cytokines, contributing to neuronal death. It is proposed that the peripheral immune system plays a pivotal role in the pathophysiology of amyotrophic lateral sclerosis (ALS). This study aims to develop an interpretable machine learning model based on blood Th1/CD4⁺ cells to predict rapidly progressive ALS.</p> Methods <p>We enrolled 564 patients with sporadic ALS who met the eligibility inclusion criteria for further analysis. Immune cells and cytokines were quantified using flow cytometric cell counting and a flow cytometry-based fluorescent bead capture assay. Multivariate Cox proportional hazards models and restricted cubic spline analyses were applied to estimate the correlation between Th1/CD4⁺ cells and rapidly progressive ALS. The important variables identified through LASSO regression analysis were incorporated into the development of the machine learning model.</p> Results <p>The multivariate Cox proportional hazards model revealed that, compared to the low Th1/CD4⁺ group (Th1/CD4⁺  &lt; 16.21), the high Th1/CD4⁺ group (Th1/CD4⁺  ≥ 16.21) was positively associated with the rate of ALS progression (HR: 1.90, 95% CI: 1.34–2.70). Th1/CD4⁺ is also associated with the decline in forced vital capacity (<i>r</i> = 0.11, <i>P</i> = 0.01). The machine learning model was built using Th1/CD4⁺ in combination with the other 4 features. Xgboost performed best in the validation cohort, achieving an AUC of 0.804 and a G mean of 0.756.</p> Conclusions <p>Th1/CD4⁺ (with an optimal cutoff value of 16.21) was established as an independent risk factor for rapid progression in ALS. The machine learning model incorporating Th1/CD4⁺ demonstrated strong predictive performance.</p> Trial registration <p>The prospective cohort study is registered with the Chinese Clinical Trial Registry (ID: ChiCTR2400079885) (<a href="http://www.chictr.org.cn/">http://www.chictr.org.cn/</a>).</p>