With increasing wastewater rates, achieving energy-neutral purification is challenging. We introduce a coral-reef-inspired Swarm Interaction Network for carbon-neutral wastewater treatment, combining morphogenetic abstraction with multi-task carbon awareness. Scalability stems from linear token complexity, mitigating the energy-removal problem. Compared with seven baselines, our approach achieves 96.7% removal efficiency, 0.31~kWh~m energy consumption, and 14.2~g~m CO emissions. Variance analysis demonstrates robustness under sensor drift. Field scenarios–insular lagoons, brewery spikes, and desert greenhouses–show potential diesel savings of up to 22%. However, data-science staffing remains an impediment. Future work will integrate AutoML wrappers within the project scope, although governance restrictions pose interpretability challenges that require further visual analytics.

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