<![CDATA[The rapid growth of digital payment technologies has expanded investment opportunities in the fintech sector while increasing decision-making complexity due to multiple criteria, including financial, technological, market, and risk factors. Conventional evaluation approaches are often inadequate to address these multidimensional challenges. This study aims to provide a structured approach for intelligent investment evaluation by integrating Machine Learning (ML) and Multi-Criteria Decision Analysis (MCDA). A Systematic Literature Review based on the PRISMA 2020 framework was conducted, analyzing 30 selected studies published between 2021 and 2026. The review identifies key evaluation dimensions and examines existing integration approaches, including the use of Analytic Hierarchy Process for weighting and TOPSIS for ranking. The findings indicate that ML enhances prediction and risk assessment, while MCDA ensures structured and interpretable evaluation, leading to improved decision quality in terms of adaptability, transparency, and analytical capability. However, the integration of ML and MCDA remains limited, as most studies apply them separately. To address this gap, this study proposes a conceptual ML–MCDA framework that provides more consistent and data-driven outcomes. The proposed framework contributes to the development of intelligent, adaptive, and reliable decision support systems for digital payment investment.]]>
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