The blistering growth of wireless networks, especially the introduction of 5G and next-generation 6G, has generated an enormous need in the management of real-time, reliable, and intelligent radio frequency (RF) channels. The traditional methods of Kalman filters, LSTMs, and heuristic loading algorithms are not suitable in prediction and load balancing on channels in highly dynamic systems, including user mobility, spectrum scarcity, and changing interference. There is a latency issue with such techniques, low adaptability and poor generalisation capabilities. This leads to shortchanging of situated possibilities of allocating the best resources, which is translated to excessive packet loss, unnecessary delays in transmission, and poor spectral efficiency. This paper deems it necessary to provide an efficient solution to these challenges by developing a new Quantum-Inspired Neural Architecture (QINA) that promises to predict the RF channel accurately. This is accompanied by an adaptive traffic distribution solution, such as Reinforcement-Guided Load Manager (RGLM). QINA operates similarly to quantum computing, utilising concepts such as superposition, entanglement, and probabilistic collapse within a classical deep learning system. It transcriptionally marks the characteristics of RF signals of quantum-like amplitude vectors, the outcome of which is the capability to perform pattern recognition and time prediction that is resilient despite analysing and unstable surroundings. Meanwhile, the RGLM can dynamically reassign traffic between multiple base stations by providing honest time feedback and Q-learning, thereby achieving balanced traffic and minimal to nonexistent congestion. It has been demonstrated to be highly effective through a deep simulation of interpolation in simulated RF environments, as well as in the DeepMIMO environment. QINA achieves 91.4% prediction accuracy, 89.3% F1-score, and reduces average latency by 23% compared to previous CNN-LSTM and Transformer-based methods. This increase is by 21% and 24% in the RGLM for the network throughput and delay, respectively. These developments demonstrate that the proposed architecture has the capacity to function optimally, scale, and be resilient in highly dynamic RF environments. By combining the theoretical aspects of quantum measurements with the practical implementation of neural network formulations, this solution presents a scalable, adaptable, and autonomous framework for real-time prediction in the spectrum and innovative resource allocation in next-generation wireless networks.