Beyond Correlation: An Explainable AI Framework for Diagnosing the Contextual Drivers of Financial Inclusion on Universal Health Coverage in the Arab World

Universal Health Coverage (UHC) remains a core Sustainable Development Goal challenge for Arab nations, with structural inequalities and conflict ongoing in threatening equal access to health. Although literature concurs on the presence of a relationship between UHC and financial inclusion, contextual mechanisms underpinning this relationship remain poorly understood. This study offers an integrated Explainable AI (XAI)–Econometric methodology to unveil how financial inclusion—through digital as well as physical channels for services—drives UHC progress in 17 Arab nations (2011–2022). With the use of Feasible Generalized Least Squares (FGLS), Panel-Corrected Standard Errors (PCSE), and Random Forest regression with SHAP (SHapley Additive exPlanations) values, we estimate both average causal and heterogeneous contextual effects. The combined model increases predictive strength by 18% over conventional econometric baselines (R2 = 0.83), while the SHAP results identified top predicators. The findings suggest that ATM density and electricity access remain the overriding drivers of UHC whereas digital financial services play a role only with the complement of strong public health expenditures. The diagnostic result allows policymakers to designate country-specific challenges and prioritize joint investments in infrastructure and inclusive finance. While data constraints in conflict settings need cautious interpretation, the proposed XAI–econometric strategy supplies a replicable methodology template for contextualized SDG diagnostics.