Abstract In this article, we introduce dual hesitant ‐rung orthopair fuzzy 2‐tuple linguistic set (DHq‐ROFTLS), a new strategy for dealing with uncertainty that incorporates a 2‐tuple linguistic term into dual hesitant ‐rung orthopair fuzzy set (DHq‐ROFS). DHq‐ROFTLS is a better way to deal with uncertain and imprecise information in the decision‐making environment. We elaborate the operational rules, based on which, the DHq‐ROFTL weighted averaging (DHq‐ROFTLWA) operator and the DHq‐ROFTL weighted geometric (DHq‐ROFTLWG) operator are presented to fuse the DHq‐ROFTL numbers (DHq‐ROFTLNs). As Maclaurin symmetric mean (MSM) aggregation operator is a useful tool to model the interrelationship between multi‐input arguments, we generalize the traditional MSM to aggregate DHq‐ROFTL information. Firstly, the DHq‐ROFTL Maclaurin symmetric mean (DHq‐ROFTLMSM) and the DHq‐ROFTL weighted Maclaurin symmetric mean (DHq‐ROFTLWMSM) operators are proposed along with some of their desirable properties and some special cases. Further, the DHq‐ROFTL dual Maclaurin symmetric mean (DHq‐ROFTLDMSM) and weighted dual Maclaurin symmetric mean (DHq‐ROFTLWDMSM) operators with some properties and cases are presented. Moreover, the assessment and prioritizing of the most important aspects in multiple attribute group decision‐making (MAGDM) problems is analysed by an extended novel approach based on the proposed aggregation operators under DHq‐ROFTL framework. At long last, a numerical model is provided for the selection of adequate medication to control COVID‐19 outbreaks to demonstrate the use of the generated technique and exhibit its adequacy. Finally, to analyse the advantages of the proposed method, a comparison analysis is conducted and the superiorities are illustrated.