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The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of users connected simultaneously. 5G uses non-orthogonal multiple access (NOMA) to increase the number of simultaneously connected users, and by encoding data it is possible to increase the spectral efficiency (SE). In this work, eight codewords are used to transmit three bits simultaneously using Sparse Code Multiple Access (SCMA), and through singular value decomposition (SVD) the Euclidean distance between constellation points is optimized. On the other hand, applications of machine intelligence and machine intelligence in 5G and beyond communication systems are still developing; in this sense, in this work we propose to use machine learning for detecting and decoding the SCMA codewords using neural networks. This paper focuses on the Use Case of enhanced mobile broadband (eMBB), where higher data rates are required, with a large number of users connected and low mobility. The simulation results show that it is possible to transmit three bits simultaneously with a low bit error rate (BER) using SVD-SCMA in the uplink channel. Our simulation results were compared against recent methods that use spatial modulation (SM) and antenna arrays in order to increase spectral efficiency. In adverse Signal-to-Noise Ratio (SNR), our proposal performs better than SM, and antenna arrays are not needed for transmission or reception. Full article