A linear Diophantine fuzzy set (LDFS) is a new mathematical tool that can be used for optimization, artificial intelligence, and process modeling. The LDFS theory widens the area of fuzzy information via “reference parameters” due to its wonderful characteristic of a broad depiction zone for allowed doublets. Because the actual world is not exact, and there is a dearth of knowledge, determining and selecting the optimal choice is a tough and unforeseen decision-making dilemma. The primary goal is to guide decision-makers through the process of selecting the best option inside a linear Diophantine fuzzy context. We suggested two new aggregation operators: the “linear Diophantine fuzzy weighted average operator and the linear Diophantine fuzzy weighted geometric operator”. Following that, the proposed model is validated using a clear example of linear Diophantine fuzzy content. This demonstrates the utility and applicability of the suggested strategy.