Illegal Wildlife Trade (IWT), a multi-billion-dollar industry, severely threatens biodiversity, human and animal health, and contributes to the spread of zoonotic diseases and invasive species. Despite policy responses, enforcement remains inadequate. Using the United States as a case study, this research applies a statistical framework combining Principal Component Analysis (PCA), hierarchical analysis, and ARIMA forecasting to evaluate IWT dynamics and policy effectiveness. Multiple socio-economic and environmental indicators were analyzed to assess their influence on trade patterns. Results show that IWT is strongly shaped by national and regional indicators, with the United States functioning as both a major importer and exporter. Visualizations of trade flows highlight persistent hotspots, while ARIMA projections over a 14-year horizon indicate a continuing upward trend, suggesting only partial effectiveness of interventions implemented after 2020. The proposed framework uncovers overlooked drivers distinguishing legal and illegal trade, providing a more robust scientific basis for targeted interventions. By integrating statistical modeling, forecasting, and visualization, the study enhances policymakers’ capacity to design adaptive strategies. Findings emphasize the urgency of stronger regulations, international cooperation, and innovative approaches to mitigate IWT’s ecological and socio-economic impacts, thereby advancing global biodiversity conservation and sustainable development.