Drought is one of the important and costliest disaster all over the world. With the accelerated progress of climate change, its frequency of occurrence and negative im- pacts are rapidly increasing. It is crucial to initiate and sustain an early warning system to monitor and predict the possible impacts of future droughts. Recently, with the rise of data driven models, various case studies are conducted by using Ma- chine Learning (ML) algorithms instead of using pure statistical approaches. The main goal of this paper is to conduct a drought forecasting study for a weather station located in Marmara Region. For that purpose, firstly, widely used univariate drought index, Standardized Precipitation Index (SPI) is calculated for Bursa sta- tion. Thereafter, both the historical information retrieved from time series data and its wavelet transformation are considered to investigate Nonlinear Auto-regressive (NAR) and Nonlinear Auto-regressive with External Input (NARX) type Neural Network (NN) models. According to a pool of goodness of fit (GOF) tests, the forecasting performance of the models with various number of hidden neurons are compared. The recent findings of the study showed that considering the data with its wavelet transformation under (NARX-NN) has benefits to increase the capacity of forecasting the drought index.