This article presents a new family of linguistic Pythagorean fuzzy aggregation operations based on Einstein t‐norms and t‐conorms. Some of their necessary properties are also discussed. In the proposed method, a generalized weighted distance measure is developed using a linguistic‐scale function to evaluate differences among linguistic Pythagorean fuzzy sets (LPFSs). An entropy measure is also introduced for LPFS to measure fuzziness associated with linguistic decision information. Moreover, a technique for ordering preference by similarity to ideal solution (TOPSIS)‐based methodology is constructed through the newly defined concepts to address linguistic Pythagorean fuzzy multicriteria group decision‐making problems, where the information about weight is either completely known or unknown. Subsequently, weights of decision makers are computed through a TOPSIS‐based algorithm, and weights of criteria are evaluated by introducing a linguistic Pythagorean fuzzy entropy weight model. To minimize information loss throughout the decision‐making procedure, aggregation is done at the final stage for obtaining the final ranking of alternatives. Finally, several practical examples are considered, solved, and compared with existing methods to exhibit the robustness of the proposed methodology. Also, a sensitivity analysis is performed to establish the dynamic nature of the proposed model.