<![CDATA[Corporate Social Responsibility (CSR) programs have become increasingly important for organizations seeking to balance strategic objectives with societal needs, yet prioritizing initiatives remains challenging due to limited resources and multiple evaluation criteria. This study aims to develop an artificial intelligence-driven multi-criteria decision model to systematically prioritize strategic CSR programs based on their social and organizational impact. Simulated CSR program scenarios representing community education, public health, environmental sustainability, and local economic empowerment were generated, and each alternative was assessed across five criteria: social impact, strategic alignment, sustainability, stakeholder benefit, and cost efficiency. The model applied a multi-criteria decision-making approach integrated with artificial intelligence techniques to assign criterion weights, calculate aggregated scores, and rank CSR program alternatives. The results indicated that social impact and strategic alignment were the most prominent criteria, with mean scores ranging from 3.60 to 4.25 on a five-point scale, whereas cost efficiency contributed the least to prioritization outcomes. The proposed model demonstrated consistent analytical behavior and enabled structured evaluation of trade-offs among competing objectives. The study contributes theoretically by extending CSR research into data-driven decision-making frameworks and practically by providing organizations with a replicable approach for evaluating and selecting CSR programs that align with both strategic and social goals. These findings suggest that integrating artificial intelligence with multi-criteria decision-making can enhance transparency, consistency, and effectiveness in corporate social responsibility management.]]>
