The primary focus of this work is to highlight a class of conditional estimators within the recursive kernel approach. In particular, we develop a recursive kernel estimator for the conditional distribution function and, consequently, for the conditional quantile. We then examine their limiting behavior by establishing the rates of almost complete convergence for both estimators. Finally, a simulation framework will manifest feasible results of the significance performance of the proposed estimator.