The challenge that faces municipal wastewater treatment plants as of today is the extreme variability in hydraulic loads and contaminant inflow caused by the constant climatic changes, aggressive urbanization, and outdated infrastructure. The traditional measures of performance that only measure mean effluent concentrations cannot capture regulatory shortcomings that present themselves in transient moments of stress. In response to this shortcoming, this paper suggests a powerful offline digital-twin model that regeneratively works on wastewater treatment, with machine-learning-comprising effluent prediction and multi-pollutant stress testing, with increased resilience indicators, and sensitivity analysis of nature-based solutions (NBS). The random-forest regressors were used to predict the suspended solids (SS), Biochemical oxygen demand (BOD) and the chemical oxygen demand (COD using large volumes of operational data. The digital twin demonstrated good predictive performance of the biochemical parameters; although the projection of SS had a relatively lower fidelity because of the stochastic nature of hydraulic shear and settling. The regulatory stress test introduced a good foundation of vulnerability particularly manifested in regard to BOD. Efficiency based NBS attenuation envelope simulated regenerative conditions to formulate the greatest attainable treatment potential. According to the results, restorative interventions are efficient in suppressing regulatory violations in relation to SS and BOD and in guaranteeing a high level of compliance among COD through moderate efficiency ranges. The sensitivity analysis indicated the non-linear resilience returns, with saturation levels attained and decreasing marginal returns to pollution reduction due to pollutant-specific returns to efficiency. In turn, the suggested framework provides a transferable decision support system that focuses on resilience-oriented planning of regenerative wastewater systems that will be able to serve effectively in uncertainty conditions.