A sufficient data length can play an important role in a proper estimation drought index, leading to a better appraisal for drought risk reduction. The South Central Region of Vietnam is one of drought prone areas but it has poor data conditions. A collection of meteorological data in the study area during a period of 38 years 1977-2014 found out a fact that there existed missing values in 10 out of 30 collected rainfall stations and 4 out of 13 collected temperature stations. Therefore, this study aims at evaluating the influence of three different infilling techniques (Inverse Distance Weighting, Multi-Linear Regression, and Artificial Neural Network) on 1-month Standardized Precipitation Evapotranspiration Index (SPEI1) drought indicator for the given region. The performance on rainfall and temperature infilling indicated that ANN technique achieved lower errors between observations and predictions than others. Infilled rainfall and temperature generated from different infilling techniques were then combined with the available data to calculate SPEI1. The results showed that infilling techniques seem to create the SPEI1 time series which have higher number of drought events, in comparison with that time series containing the observation values only. Otherwise, drought index seems to be insensitive to different infilling techniques.