The major goal of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is to get effective channel state information (CSI). Most of the recent works use nuclear norm theory for recovering the low-rank scheme of channels. Some suboptimal solutions to the rank minimization problem can occur while addressing the nuclear norm-based convex problem, which degrades the accuracy of channel estimation. Some works recover the channel with the assumption of the mmWave channel using an over-complete dictionary. On the other hand, the accuracy of available CSI may openly influence the efficiency of mmWave communications. The main intention of this paper is to develop an enhanced channel estimation model with an optimized hybrid deep learning model. Here, the integration of deep neural network (DNN) and long short-term memory (LSTM) form the hybrid deep learning model termed optimized D-LSTM, which is modified by the opposition searched explorationbased Harris hawks optimization (OE-HHO). The input to the proposed hybrid deep learning is taken as the correlation among the received signal vectors and the measurement matrix for predicting the beam space channel amplitude.Finally, the successful channel estimation is observed by deep hybrid learning by the experimental outcomes, which also demonstrate that the proposed channel estimation model overwhelms the conventional models in terms of Normalized Mean-Squared Error (NMSE) and spectral efficiency. The experimental results show that the designed OE-HHO method obtains 9.2%, 8.9%, 8.65%, and 0.47% progressed than DA, DHOA, GWO, and HHO, respectively. Therefore, higher efficiency is observed by OE-HHO based mmWave MIMO communication system.