Sanitary Sewer Overflows (SSO) caused by excessive Rainfall Derived Infiltration and Inflow (RDII) is the major challenge currently faced by municipal administrations, and therefore, the ability to correctly predict the wastewater state of the sanitary sewage system in advance is especially significant. In this paper, we present the design of the Sparse Autoencoder-based Bidirectional Long Short-Term Memory (SAE-BLSTM) network model, a model built on Sparse Autoencoder (SAE) and Bidirectional Long Short-Term Memory (BLSTM) networks to predict the wastewater flow rate in a sanitary sewer system. This network model consists of a data preprocessing segment, the Sparse Autoencoder (SAE) network segment, and the Bidirectional Long Short-Term Memory (BLSTM) network segment. The SAE is capable of performing data dimensionality reduction on high-dimensional original input feature data from which it can extract sparse potential features from the aforementioned high-dimensional original input feature data. The potential features extracted by the SAE hidden layer are concatenated with the smooth historical wastewater flow rate features to create an augmented previous feature vector that more accurately predicts the wastewater flow rate. These augmented previous features are applied to the BLSTM network to predict the future wastewater flow rate. Thus, this network model combines two kinds of abilities, SAE's low-dimensional nonlinear representation for original input feature data and BLSTM's time-series prediction for wastewater flow rate. We then conducted extensive experiments on the SAE-BLSTM network model utilizing the real-world hydrological time-series datasets. The experimental results show that our proposed SAE-BLSTM model consistently outperforms the advanced comparison models.