In large enterprises, optimizing financial management in digital marketing campaigns is a complex, multi-dimensional challenge that requires balancing multiple criteria, such as budget allocation, return on investment (ROI), and risk mitigation. This study presents a novel approach to optimizing digital marketing strategies using fuzzy decision-making systems and multi-criteria decision-making (MCDM) algorithms, specifically the TOPSIS method. A comprehensive dataset of 500 marketing campaigns was analyzed, capturing key financial variables such as budget, cost per click (CPC), click-through rate (CTR), conversion rate, customer lifetime value (CLV), and risk levels. The results demonstrate how fuzzy logic can be applied to assess and minimize financial risks while maximizing returns and engagement. Key insights were visualized through a 3D surface plot of budget, conversion rate, and ROI, and a box plot illustrating the relationship between engagement levels and risk. The TOPSIS algorithm ranked campaigns based on their financial performance, showing clear distinctions between high- and low-performing strategies. Sensitivity analysis further illustrated the effects of budget allocation on ROI and conversion rates, providing a holistic view of the optimization process. The study contributes to the field of financial management in digital marketing by demonstrating how fuzzy logic and MCDM approaches can drive data-driven decision-making in large enterprises. The findings have significant implications for strategic budget allocation, risk management, and campaign prioritization in the context of large-scale digital marketing efforts.