Uplift Modeling aims to detect subgroups in a population with a specific response or reaction to an action taken on the targeted group. In these models, the Treatment set contains objects that have been exposed to some action, such as a marketing campaign or clinical treatment, while in the Control set, they have not. In this study, a novel ARTIFICIAL IMMUNE SYSTEM-based model was designed using an AIRS classifier to solve uplift modeling problems with improved efficiency. In this approach, a predictive model was built for estimating the conditional probability of receiving the desired response from the subpopulation that has taken the action over the relevant probability of the sub-population that has not taken the action. The proposed model was tested on the Hillstorm-visit-w dataset. Experimental results showed a 138 percent improvement in the area under the uplift curve which is a measure to assess an uplift model's performance.