One of the major challenges in multiple input multiple output–orthogonal frequency division multiplexing (MIMO–OFDM) systems is inference, noise, and effective channel estimation (CE). To overcome these challenges, a deep learning (DL) approach, a complex convolutional neural network, along with polar coding (C-CNN), is proposed in this research. The use of polar coding (PC) and polar decoding (PD) contributes to improved bit rate error (BER) and increased data rate in Fifth-Generation (5G) networks. The optimized C-CNN-based CE is incorporated into the receiver of polar-coded MIMO–OFDM for solving noise and inference in the system. The proposed approach identifies the signals precisely at the receiver and also improves the identification of real channel properties based on least squares (LS) estimations. The simulation using MATLAB is performed with the help of DL and 5G toolboxes to get results in terms of different channel characteristics, BER, and mean squared error (MSE). The proposed C-CNN-CE has achieved of 98.54% of accuracy in polar-coded CE, 13.55% of BER, and 18.73% of MSE.