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High energy demands and limited adaptability in dynamic environments often constrain traditional communication systems. This work proposes a neuromorphic communication framework that leverages brain-inspired mechanisms to enhance efficiency and responsiveness. Using spiking neural networks (SNNs) with the Leaky Integrate-and-Fire (LIF) neuron model, the system transmits information only when events occur, reducing unnecessary signaling. Hebbian learning is introduced to enable adaptive behavior, while neuromorphic hardware platforms such as Intel Loihi and IBM TrueNorth are discussed as potential enablers for real-time implementation. A comparison with conventional approaches highlights the potential benefits of lower power consumption and improved adaptability. The study is theoretical but provides a structured foundation for communication systems designed for IoT, 6G networks, and sustainable applications.