Innovation is a fundamental driver of economic growth, regional competitiveness, and societal well-being. Nonetheless, its benefits remain unevenly distributed, with many regions, so called "innovation deserts," struggling to translate potential into productivity due to systemic barriers and limited insight into the drivers of innovation. Motivated by this disparity, this article presents a novel data-driven framework for analyzing and benchmarking innovation productivity across the U.S. counties, identifying key determinants that drive regional innovation performance. The study covers over 3200 U.S. counties and includes 108 attributes spanning innovation outputs, demographics, education levels, STEM participation, internet connectivity, business sizes, and economic indicators. The framework comprises three integrated modules: 1) a hybrid feature selection mechanism combining random forest importance scores with mutual information criteria to identify influential factors; 2) a nonlinear dimensionality reduction module employing kernel-based principal component analysis to mitigate the curse of dimensionality; and 3) a particle swarm optimized XGBoost model (PSO-XGBoost) enabling sensitivity analysis and scenario-based forecasting. The framework demonstrates superior predictive performance (R-2 >0.81) compared to five other state-of-the art boosting algorithms. The analysis uncovers nonlinear relationships between key attributes and innovation outcomes, facilitating benchmarking of innovation productivity and quantifying targeted interventions to foster inclusive innovation ecosystems. The proposed framework offers policymakers interpretable, actionable insights for evidence-based regional innovation development. In particular, this study contributes a novel framework to identify innovation deserts and benchmark regional performance at a national scale.