Complex, non-linear human connections contribute to the significant impact that the inherently unpredictable nature of environmental factors (EF) has on the behavior of environmental populations. To forecast climate-driven population changes, this study proposes a hybrid model combining Statistical Learning (SL) with Stochastic Differential Equations (SDE). For the aim of Feature Selection (FS) and the detection of temperature anomalies, this investigation applies Mutual Information and Bayesian Data Augmentation (MI + BDA). The Earth Solar System (ENSO) index has been identified as the primary predictor. This work implements stochastic forcing variables that characterize ecological risk in the climate-coupled SDE. Forecasting methods have been reported to have lower predictive accuracy (R² = 0.92, RMSE = 0.12) than the proposed model, as tested with the North Atlantic cod and Adélie penguin datasets. The environment faces significant temperature constraints, according to this investigation and other studies on sustainable development. The empirical evidence indicates that a 78% increase in the Risk of Death (RoD) over 52 years is associated with temperatures exceeding 3.5°C. Maintain awareness that this study proves the value of non-linear climate-population correlations, which are vital to the maintenance of biodiversity.