This paper investigates predictive inference for individual treatment effects (ITEs) using machine learning techniques. Traditional approaches have primarily concentrated on developing meta-learners for estimating the conditional average treatment effect (CATE), offering point estimates without considering predictive intervals. The study introduces conformal meta-learners, a framework that enhances traditional CATE meta-learners by applying the conformal prediction procedure to provide predictive intervals for ITEs. This method is validated through a stochastic ordering framework, highlighting that conformal meta-learners can achieve valid inferences with desired coverage levels.