The Bonferroni mean (BM) operator, introduced by Bonferroni, is a powerful tool to capture the interrelationship among aggregated arguments. Various generalizations and extensions of BM have developed and applied to solve many real-world problems Recently, the notion of Partitioned Bonferroni mean (PBM) operator has been proposed with the assumption that the interrelationships do not always exist among all of the attributes. This work studies the PBM operator under triangular fuzzy environment. first, we propose a new fuzzy, aggregation operator called the triangular fuzzy partitioned Bonferroni mean (TFPBM) operator for aggregating triangular fuzzy, numbers. Some properties and special cases of the new aggregation operator are also investigated For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted partitioned Bonferroni mean (TFWPBM) operator. Furthermore, based on TFWPBM operator, an approach to deal with multiple attribute decision-making problems under triangular fuzzy environment is developed. Finally, a practical example is provided to illustrate the developed approach.