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Deep learning (DL) techniques can significantly improve successive interference cancellation (SIC) performance for the non-orthogonal multiple access (NOMA) system. The NOMA-orthogonal frequency division multiplexing (OFDM) system is considered in this paper to develop a hybrid deep neural network (HyDNN) model for multiuser uplink channel estimation (CE) and signal detection (SD). The proposed HyDNN uses a combination of a bi-directional long short-term memory (BiLSTM) network and a one-dimensional convolutional neural network (1D-CNN) to optimize errors in the system. The extraction of input signal characteristics from OFDM is carried out using the 1D-CNN model and fed into the time series BiLSTM network to infer the signal at the receiver terminal. The HyDNN model learns through the simulated channel data during offline training. To optimize the loss during learning the model the Adam …