<p>This cross-national study examines the determinants of AI translation technology adoption among professional translators in Morocco, Saudi Arabia, and Yemen. Employing an integrated TAM/UTAUT framework extended with profession-specific constructs (e.g., perceived risk/trust, barriers and concerns, professional impact), we investigated how cognitive, contextual, and professional factors influence AI tool adoption in linguistically complex Arab contexts. Using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4.0, our analysis revealed that Perceived Usefulness and Performance Expectancy were the strongest positive predictors of adoption. Facilitating Conditions also significantly promoted use, while Barriers and Concerns, such as those regarding accuracy and confidentiality, exerted a significant negative influence. The research model demonstrated substantial explanatory power, accounting for 66.7% of the variance in actual use. A multi-group analysis revealed distinct cross-national profiles: Saudi Arabia exhibited an infrastructure-dependent pattern of adoption, Yemen demonstrated a utility-focused pragmatism, and Morocco emphasized performance benefits and trust. These findings demonstrate that adoption mechanisms vary systematically across developmental and institutional contexts rather than operating uniformly. The research provides an empirical foundation for context-sensitive interventions, such as infrastructure investment in well-resourced environments, trust-building in competitive markets, and practical utility demonstration in resource-constrained settings, thereby advancing both technology acceptance theory and translation studies scholarship.</p>