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A unified artificial intelligence framework integrating Graph Neural Networks, Generative Adversarial Networks, Reinforcement Learning, Green Chemistry optimization, and Physics Informed Neural Networks with embedded Darcy\u2019s law and a hybrid AI physics model. This framework simulates contaminant transport, generates climate scenarios, and optimizes sustainable remediation strategies across four calibrated environmental scenarios. Validation employed four synthetic environmental scenarios with parameters calibrated from documented PFAS contamination studies, representing controlled algorithm development prior to field deployment (noise sigma 1.5 to 4.0\u00a0mg per liter; seasonal amplitude 0.1 to 0.3; trend 0 to 0.1\u00a0mg per liter per day). The hybrid AI physics model achieved 89% predictive accuracy on synthetic validation datasets with literature-calibrated parameters, outperforming traditional (65%), pure AI (78%), and physics-only (72%) approaches under controlled synthetic conditions. Graph Neural Networks captured complex spatiotemporal patterns (R\u00b2 &gt; 0.89), while Reinforcement Learning optimization improved simulated treatment efficiency from 62.3% to 89.7% in synthetic remediation scenarios. Green Chemistry optimization modeling suggested supercritical carbon dioxide and ionic liquids as potentially efficient solvents, with model-predicted efficiencies of 88% to 92% and relative toxicity scores between 1.8 and 2.1 units (where lower scores indicate reduced environmental impact). Physics Informed Neural Networks reduced physics loss from approximately 1.2 to 0.03\u2009\u00b1\u20090.005, achieving convergence at a total loss of 0.08\u2009\u00b1\u20090.01 over 50 training epochs on synthetic datasets. The framework demonstrated computational scalability from 80 to 5000 synthetic records and estimated deployment feasibility within a 22-month timeline under optimal conditions, contingent upon successful field validation. SHAP and LIME analyses indicated natural attenuation, particularly the decay process, as the most influential model feature, contributing a mean SHAP value of 0.34\u2009\u00b1\u20090.08, consistent with expected physical processes. 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