To investigate effect of combined multifactorial prediction on the prognosis of patients with acute ischemic stroke (AIS) on the precise use of medication, and to predict the prognosis of patients with AIS on the precise use of medication by developing a combined multifactorial model. Methods: A prospective cohort study design was used to recruit patients at department of neurology in our hospital from March 1, 2019 to December 30, 2020, with a follow-up at the 90th d after hospital discharge. The modified Rankin Scale (mRS) at 90-d follow-up was divided into a good prognosis group (mRS score 0～2) and poor prognosis group (mRS score 3～6), and markers affecting the prognosis of patients with precision medication AIS were analyzed using multifactorial Logistic regression; ROC curves were used to verify the diagnostic validity of the Logistic regression model, and Delong test was performed to evaluate the difference in the area under the curve. Results: A total of 240 patients were recruited for the study and 221 patients completed the follow-up; of these, 168 and 53 were in the good and bad prognosis groups, respectively. Multi-factor Logistic regression analysis showed that interleukin-6 (IL-6) levels and admission NIHSS scores were independent risk factors for patient prognosis, and their combined predictive value was higher than the single variable predictive effect. Conclusion: Serum IL-6 and admission NIHSS scores can be used as predictors of prognosis for patients with precision medication AIS and can better predict the prognosis of AIS patients at 90 d by a combined multifactorial prediction model for early assessment of prognosis in AIS patients.