This　article　is　concerned　with　the　estimation　problem　in　the　semiparametric　isotonic　regression　model　when　the　covariates　are　measured　with　additive　errors　and　the　response　is　missing　at　random.　An　inverse　marginal　probability　weighted　imputation　approach　is　developed　to　estimate　the　regression　parameters　and　a　least-square　approach　under　monotone　constraint　is　employed　to　estimate　the　functional　component.　We　show　that　the　proposed　estimator　of　the　regression　parameter　is　root-n　consistent　and　asymptotically　normal　and　the　isotonic　estimator　of　the　functional　component,　at　a　fixed　point,　is　cubic　root-n　consistent.　A　simulation　study　is　conducted　to　examine　the　finite-sample　properties　of　the　proposed　estimators.　A　data　set　is　used　to　demonstrate　the　proposed　approach.