As the world moves closer to the development of 6G communication networks, there is an essential need to discover answers to the issues brought on by the growing demand for ultra-low latency, massive connections, and ever-increasing data rates. The authors of this paper propose to use deep learning to enhance the estimations of the 6G wireless channel state in light of it. Within the communication chain, a source encoder efficiently encodes the input information, and a channel encoder adds redundancy for error correction. The encoded data is sent into a symbol mapper in order to be rendered suitable for transmission over the communication channel. The new component of this study is a DL-based channel estimator. The channel estimator consists of two crucial steps: obtaining channel data as well as learning from data. The acquisition step gathers channel data in real-time to fuel the subsequent data-driven learning phase. By employing deep learning techniques to learn the intricate characteristics of the communication channel and be able to adjust to changes in real time, the system maximizes the estimation process. The DL-based channel predictor is used to modify the anticipated channel output in order to increase the communication system’s overall performance and dependability. The proposed approach exhibits flexibility to changing channel circumstances and enhances the accuracy of the channel estimation, making it well-suited for real-world communication scenarios. For this work, MATLAB is the program of choice.