Digital transformation powered by artificial intelligence and mathematics has become the defining force of global competitiveness. This study examined how mathematical innovation shapes digital transformation performance across nations and industries. It adopted a quantitative design using Structural Equation Modeling on secondary datasets from the S&P Global 1200, OECD, UNESCO, and World Bank covering 2020 to 2024. The findings showed that algorithmic optimization, predictive computation, and mathematical modeling efficiency significantly influence automation, decision accuracy, and innovation productivity. The estimated structural model yielded strong statistical support with coefficients β1 = 0.41, β2 = 0.36, and β3 = 0.33 (p < 0.01), confirming that mathematical determinants drive measurable transformation outcomes. The results also revealed that AI integration intensity positively moderates these relationships, magnifying the global effect of mathematical capability. This research contributes to theory by extending the Unified Theory of Acceptance and Use of Technology through the addition of mathematical innovation and AI integration intensity, thereby broadening its explanatory scope and offering a refined framework for understanding digital transformation in global settings. The study connects to global debates on how data science, computational literacy, and institutional AI readiness shape digital economies. It recommends that policymakers treat mathematical capability as a strategic digital asset, firms embed algorithmic design into operations, and educators align curricula with computational transformation demands. The findings provide theoretical, managerial, and policy pathways for advancing data-driven transformation across regions.