Intuitionistic multiplicative sets, an extension of multiplicative preference relations, incorporate both asymmetrical and non-uniform membership and non-membership degrees, making them particularly useful for handling uncertainty in decision-making problems. These sets have been widely studied in the literature and applied across various domains, including engineering, economics, and artificial intelligence. In this study, we extend the Multi-Attributive Border Approximation Area Comparison (MABAC) method, a well-established multi-criteria decision-making (MCDM) approach, by integrating intuitionistic multiplicative set elements into its framework. This extension enhances the MABAC method’s ability to process decision-making problems involving uncertain, asymmetrical, and non-uniform data, making it a more robust and flexible approach in complex decision environments. To demonstrate the effectiveness of the proposed intuitionistic multiplicative MABAC (IM-MABAC) method, a numerical example is presented, illustrating its applicability in a real-world decision-making scenario. Furthermore, a comparative analysis with other well-known MCDM methods, such as intuitionistic multiplicative TOPSIS (IM-TOPSIS) and intuitionistic multiplicative TODIM (IM-TODIM), is conducted to validate its reliability and consistency. The results indicate that the intuitionistic multiplicative MABAC method provides a structured and systematic decision-making framework, offering an alternative approach for handling imprecise and uncertain information in multi-criteria problems.