This paper proposes a new framework combining distributed collective intelligence and deep neural networks (DNNs) for multiobjective optimization in wireless systems of future generation. This paper mitigates some of those disadvantages by proposing a distributed consensus algorithm that incorporates predictive energy allocation and power‐based learning, as well as multiagent coordination. The enhanced long short‐term memory (LSTM) networks and swarm intelligence principles are used within the framework to predict dynamic load and allocate resources. Mathematical derivations and extensive Monte Carlo simulations ( n = 1000) demonstrate that the prediction accuracy gets up to 85.3%, saving energy at most 25.7%, improving throughput about approximately 32.4%, which reduces latency by nearly 38.6% and the packet loss rate decreases over the figure of approximately 45.2%. Analysis with 95% confidence intervals demonstrates robustness in a variety of scenarios, such as multiuser operation (10–1000 users) and various SNR conditions (−10~30 dB). The distributed collective intelligence technique outperforms centralized ones by 18.5% while keeping the system scalable and robust. Extensive comparisons with the state‐of‐the‐art methods achieve more efficient performance in terms of energy efficiency, convergence rate, and adaptability to dynamic network scenarios.
Kadhim et al. (2026) studied this question.