This paper aims to study the potential biological mechanism of Üstikuddus Sherbiti (ÜS) in the treatment of ischemic cerebrovascular diseases (ICVD) by the network pharmacology method. Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to obtain effective constituents of ÜS by screening eligible oral utilization, drug similarity, and blood-brain barrier permeability threshold. By drug target prediction and stroke treatment target mining, 2 target data sets were analyzed to find intersection targets and the corresponding constituents were used as active constituents. An active constituent target network and an effective constituent target network were constructed by using Cytoscape 3.7.2 software. Degree parameters of the effective constituent target network were analyzed to find important effective constituents and targets. Through protein-protein interaction (PPI) analysis/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, potential signaling pathways of ÜS in ischemic stroke were found out. AutoDock was used for molecular docking verification. A total of 90 active constituents of ÜS were screened out. There were 10 active constituents against ICVD, including quercetin, luteolin, kaempferol, and naringenin, and 10 important targets for anticerebral ischemia, namely, PIK3CA, APP, PIK3R1, MAPK1, MAPK3, AKT1, PRKCD, Fyn, RAC1, and NF-κB1. Based on the protein interaction network, the important targets of ÜS were significantly enriched in PI3K-Akt signaling pathway, neuroactive ligand-receptor interaction pathway, Ras signaling pathway, etc. ÜS in ICVD has characteristics like multiple targets, multiple approaches, and multiple pathways. Results of molecular docking showed that the active components in ICVD had a good binding ability with the key targets. Its main biological mechanism may be related to the PI3K-Akt and Ras-MAPK centered signaling pathway. Our study demonstrated that ÜS exerted the effect of treating ICVD by regulating multiple targets and multiple channels with multiple components through the method of network pharmacology and molecular docking.