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In this work, we investigate the end-to-end performance of the uplink (U/L) non-orthogonal multiple access (NOMA) system based on the novel Bi-directional long short-term memory (Bi-LSTM) algorithm over the frequency flat independent and identically distributed (iid) Rayleigh fading channel conditions. When compared to conventional successive interference cancellation (SIC) MIMO-NOMA-based detection systems, the suggested deep learning (DL) based technique integrates the conventional multiple-input multiple-output (MIMO) and power-domain NOMA schemes to improve the symbol error rate (SER) performance. To this end, an optimal power allocation problem for the MIMO-NOMA scheme has been developed that maximizes the data throughput of the end-to-end system. In this work, both imperfect and perfect SIC schemes are considered, and performance comparison is provided between the Bi-LSTM based MIMO-NOMA and LSTM MIMONOMA schemes. The SIC NOMA system achieves 15 dB for 106 iterations, but the DL-based MIMO-NOMA scheme achieves 15 dB for 100 iterations. By a factor of four, Bi-LSTM MIMO-NOMA schemes outperform SIC MIMO-NOMA methods. Rather than utilizing conventional SIC systems to determine fading channel coefficients and decode signals, the suggested scheme estimates the relevant data symbol using the more efficient Bi-LSTM algorithm. There is a 4 dB difference, indicating that DL-based MIMO-NOMA outperforms conventional SIC MIMO-NOMA approaches. Furthermore, when the channel estimation error is enhanced from 0 to 1, the performance of DL is considerably decreased …