(6G) conversation networks promise extra pace, coverage and capabilities than the current era (5G). To fulfill this promise, deep getting to know techniques can be used to allow better useful resource allocation choices. Deep mastering approaches can gather temporal correlations between node positions and sign characteristics to predict system variables over the years. This statistics may be used to higher control community sources, optimize spectral performance, and decrease latency. Improved resource allocation can also allow smarter visitors routing and selection making, specifically in terms of predicting consumer’s options and wishes. The proposed version uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enable seamless community connectivity. To determine resource allocations autonomously, it utilizes converting community topologies, consumer mobility, and a ramification of person site visitor’s profiles. The version optimizes resource allocation and cargo balancing in order to maximize throughput and decrease latency whilst still preserving machine stability. This permits the verbal exchange community to be greater adaptive while also increasing its efficiency. Ultimately, the proposed deep gaining knowledge of-primarily based aid allocation method provides a singular method to improve the overall performance and scalability of 6G verbal exchange networks.