The illegal wildlife trade (IWT) poses a significant global challenge that threatens biodiversity and ecosystem balance. This study addresses these complexities by proposing the Integrated Ecological Intervention Optimization Model (IEIOM). The model integrates three core metrics—habitat area, crime rate, and quantity of IWT—while incorporating multidimensional analysis and predictive modeling across ecological, social, and economic dimensions. To enhance predictive accuracy, we employed nonlinear regression, grey prediction, and autoregressive models. These predictive insights, combined with empirical data, were integrated into a multi-index intervention optimization framework using a sum-of-sines function. A simulated annealing algorithm was subsequently applied to achieve global optimization. Results indicate that the proposed IEIOM outperforms the traditional entropy weight method by providing a more dynamic, data-driven weight allocation. The optimal weights prioritized crime suppression (50%), habitat protection (28%), and trade regulation (22%), underscoring the critical roles of law enforcement and environmental preservation. Sensitivity analysis further demonstrated that technological innovation, community collaboration, and public awareness are pivotal to successful interventions. Overall, the IEIOM provides a robust decision-support tool for policymakers, enabling effective resource allocation to combat IWT and contributing to long-term sustainable development.