<p>Simulating uncertainty for dealing with actual events is one of Artificial Intelligence's key difficulties and challenges. The ultimate objective for decision-makers is to manage uncertainty, particularly in indeterminate scenarios when it is not the case that the solution of the problem can be expressed with true or false values. As a result, new techniques to facilitate the interpretation of indeterminacy are currently under development. Neutrosophic logic (NL), which addresses the concept of neutralities, extends classic logic, fuzzy logic, paraconsistent logic, intuitionistic logic, and so on. The single-valued neutrosophic set (SVN) is a subclass of neutrosophic sets that has recently been presented. Solving multicriteria decision-making problems is an essential use case for SVNs to be applied. The research objectives of the article are twofold. First, we examine the potential of utilizing single-valued neutrophilic sets in a more efficient manner to address the issue of multicriteria decision-making. Within this framework, our aim is to explore and extend the concept of information quality as an uncertainty measure by comparing it to neutrosophic Dempster–Shafer (D–S) evidence theory in the context of decision-making. As a proposed solution to the aforementioned research objectives, this manuscript suggests and implements a novel conceptual framework for determining and quantifying the similarity measure between SVNSs in a multicriteria decision-making context under the principles of D–S evidence theory. Finally, illustrative case studies are given to support the logic and practicality of the suggested methodology compared to current methodologies.</p>