Optimization algorithms are reshaping business intelligence systems, yet fragile economies like Iraq face uneven benefits. This study examined Iraq from 2020 to 2024 to assess how algorithm type, integration scope, and industry application influenced analytical accuracy, decision speed, efficiency, and predictive insights under contextual constraints. A descriptive and explanatory research design was applied, using 105 secondary data cases analyzed with descriptive statistics, correlation, and regression. Results show linear programming adoption grew from 14 to 38 percent, genetic algorithms from 7 to 28 percent, and hybrids from 5 to 27 percent. Accuracy improved by 23 percent, decision speed by 25 percent, efficiency by 22 percent, and predictive insight by 23 percent. Correlation analysis confirmed strong associations with algorithm type (0.82), integration scope (0.77), and industry application (0.71), while contextual constraints showed a negative correlation (-0.55). Regression revealed algorithm type had the strongest effect (β = 0.43), followed by integration scope (β = 0.32) and industry application (β = 0.24), with constraints eroding gains (β = -0.20). The model explained 84 percent of the variation in outcomes, proving optimization algorithms are decisive for BI advancement. The results imply Iraq must diversify algorithm adoption, expand enterprise and real-time integration, and address infrastructure and data gaps to achieve sustainable competitiveness. Recommendations stress wider training, investment in SMEs, stronger governance, and infrastructure expansion.