Estimation of the treatment effect parameter is one of the crucial problems with clinical

trials for two or multiple treatments. The covariate-adaptive randomization methods are often applied to balance treatment assignments across prognostic factors in clinical trials, such as the minimization and stratified permuted blocks method. We propose the machine learning and bayesian machine learning estimator of average treatment effects under covariate-adaptive randomization methods. The proposed machine learning estimator is consistent and asymptotically normally distributed. Simulation studies show that the proposed machine learning and Bayesian machine learning estimators are comparable with Ye’s estimator, and it performs better than the Bugni’s estimator and unadjustment estimator when the outcome model is linear models. The proposed estimator has some advantages over the Bugni’s estimator and Ye’s estimator in terms of the standard error and root mean squared error when the outcome model is nonlinear models. The proposed method is robust for the form of outcome model. Finally, we apply the proposed methodology to a data set that studies the treatment effect of insurance provision on tobacco production for 12 tobacco producing counties in Jiangxi Province, China during 2003.