<p>The technological revolution is reshaping the current realm of finance and economics. This change is now driven by the concept of Central Bank Digital Currency (CBDC), with central banks worldwide beginning pilot programs and implementations. It has sparked a debate on the pros and cons of digital currencies in the mainstream economy, specifically centered on the issue of privacy. Against this backdrop, this study investigates the behavioral intention to accept CBDC by extending the Unified Theory of Acceptance and Use of Technology (UTAUT). It also includes two novel constructs: Perceived Anonymity (PA) and Digital Financial Literacy (DFL), along with Perceived Privacy Risk (PPR) as a mediating construct. This study followed a multi-analytical approach, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Network (ANN), and Importance-Performance Map Analysis (IPMA) in a cross-sectional survey of 397 mobile payment users. PLS-SEM results revealed that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), DFL, and PA significantly influence the adoption intention of CBDC. At the same time, PPR did not mediate the PA ~ Behavioral Intention (BI) link. The model explained 66% of the variance (R² = 0.66), indicating strong explanatory power. ANN analysis validated these relationships, confirming EE and DFL as the strongest non-linear predictors of adoption. IPMA results further demonstrated that although EE and DFL are critical for behavioral intention, their relatively lower performance highlights priority areas for policy and design interventions. By integrating privacy-centric and literacy-oriented constructs into the UTAUT framework, this research makes a significant contribution to the theory of FinTech adoption. The study also provides policymakers with actionable insights to enhance DFL and embed robust anonymity safeguards, thereby fostering user trust in CBDC systems.</p>