While the rollout of 5G cellular networks will extend into the next decade, there is already significant interest in the technologies that will form the foundation of its successor, 6G. Although 5G is expected to revolutionize our lives and communication methods, it falls short of fully supporting the Internet of Everything (IoE). The IoE envisions a scenario where over a million devices per cubic kilometer, both on the ground and in the air, demand ubiquitous, reliable, and low-latency connectivity. 6G and future technologies aim to create a ubiquitous wireless connectivity for entire communication system. This development will accommodate the rapidly increasing number of intelligent devices and communication demand. These objectives can be achieved by incorporating THz band communication, wider spectrum resources with minimized communication error. However, this communication technology faces several challenges such as energy efficiency, resource allocation, and latency which needs to be addressed to improve the overall communication performance. To overcome these issues, we present a roadmap for Point-to-Point (P2P) and Point-to-Multipoint (P2MP) communication where channel coding mechanism is introduced by considering turbo-channel coding scheme as base approach. Furthermore, deep learning-based training is provided to improve the error-correcting performance of the system. The performance of proposed model is measured in terms of BER for varied SNR levels and additive white noise channel distribution scenarios, where experimental analysis shows that the proposed coding approach outperformed existing error-correcting schemes.