Weather forecasting (especially rainfall) is one of the most important and challenging operational tasks carried out by meteorological services all over the world. Itis furthermore a complicated procedure that requires multiple specialized fields of expertise. In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of Homs Meteorological Station on accounts of the preceding events of rainfall data. The feed-forward neural network with back-propagation Algorithm is used in the learning and forecasting, where the time series of rain that detailed transactions and the approximate three levels of analysis using a Discrete wavelet transform (DWT). The study found that the neural network WNN structured )5-8-8-8-1(, able to predict the monthly rainfall in Homs station on the long-term correlation of determination and root mean squared-errors (0.98, 7.74mm), respectively. Wavelet Transform technique provides a useful feature based on the analysis of the data, which improves the performance of the model and applied this technique in ANNmodels for rain because it is simple, as this technique can be applied to other models.