The senescence-associated secretory phenotype (SASP) is a key mechanism through which senescent cardiovascular cells contribute to plaque formation, instability, and vascular remodeling. However, the correlation between SASP and acute ischemic stroke (AIS), particularly its immune inflammation characteristics, remains underexplored and requires further elucidation. We downloaded the AIS database from the GEO database and obtained SASP genes from the SASP Atlas and related literature. Using two machine learning algorithms, we identified five hub genes. Unsupervised cluster analysis was performed on patients with AIS and DEGs separately to identify distinct gene clusters, which were then analyzed for immune characteristics. We then explored the related biological functions and immune properties of the hub genes by using various algorithms (GSEA, GSVA, and CIBERSORT). Principal component analysis (PCA) was used to generate SASP-related gene scores based on the expression of hub genes. A logistic regression algorithm was employed to establish an AIS classification diagnosis model based on the hub genes. Peripheral venous blood was collected for validation using real-time quantitative PCR (RT-qPCR). We identified five hub genes using two machine learning algorithms and validated them with RT-qPCR. Gene cluster analysis revealed two distinct clusters, SASP-related gene cluster B and differentially expressed gene cluster B, indicating that the acute AIS samples had more severe immune inflammatory response and a higher risk of disease deterioration. We constructed a gene-drug regulatory network for PIN1 and established an AIS diagnostic model and nomogram using a logistic regression algorithm. This study explored the gene expression, molecular patterns, and immunological characteristics of SASP in patients with AIS using bioinformatic methods. It provides a theoretical basis and research direction for identifying new diagnostic markers for AIS, understanding the molecular mechanism of thrombosis, and improving the classification, diagnosis, treatment, and prognosis of AIS.