Handling the missing values play important step in the preprocessing phase of hydrological modeling analysis. One of the challenges in preprocessing phase is to deal with the problems of missing data with good consideration on the pattern and approaches of the missing data. Hence, this paper presents a study on Feedforward neural network algorithm (FFNN) and Elman neural network (ENN) imputation algorithm in estimating missing rainfall data at different percentages of missingness. Reliable rainfall data series from nearest neighbor gauging stations were used as inputs to predict the missing rainfall data for an output station. The selected study area is Sungai Merang, East Malaysia. The study revealed that ENN method demonstrated a superior prediction of the missing daily rainfall data than FFNN method. It is also observed that the ENN model-infilling method could be highly beneficial in reducing the data gaps for continuous hydrological modelling analysis.