Shrimp-rice systems in Vietnam’s Mekong Delta face volatile water quality, rising energy costs, and underutilized nutrient waste. We present a hybrid fuzzy-GNN-MOERL-circular framework that unifies prediction and prescription for sustainable control. First, fuzzy spatiotemporal utility mining extracts interpretable early-warning rules under sensor uncertainty. Second, a graph neural encoder captures inter-pond/canal dependencies to form compact states. Third, multi-objective evolutionary RL (NSGA-II/MOEA-D) discovers Pareto policies that balance yield, energy, CO2, and reuse, with fuzzy rules enabling safety-aware reward shaping and action masking. Finally, a graph-based circular-allocation module optimizes sludge/effluent routing to rice fields and biogas units. On ten Mekong farms, our approach improved early-warning F1 by +19% with -31% false alarms, and achieved -18% energy and -22% CO2 versus baselines with minimal yield trade-off. Circular allocation raised sludge reuse from 45 → 83% while cutting transport cost by 26%. An XAI dashboard (SHAP + rule summaries) increased expert trust and auditability. The results demonstrate a practical, explainable pathway to low-carbon, circular aquaculture at cooperative scale.