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For choosing the optimal option for multiple issues, the multiattribute decision-making (MADM) technique within a Fermatean fuzzy environment is a well-known and significant method. This paper presents a novel superiority inferiority ranking (SIR) approach for Fermatean fuzzy sets in group decision-making using multicriteria to reduce investment risk. This approach aims to evaluate the strategies for selecting the optimal investment company. The SIR method is depicted, and its effectiveness in decision-making is explored. In this manuscript, we develop new types of A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">c</mi><mi mathvariant="script">z</mi><mi mathvariant="script">e</mi><mi mathvariant="script">l</mi></mrow></semantics></math></inline-formula>–A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">l</mi><mi mathvariant="script">s</mi><mi mathvariant="script">i</mi><mi mathvariant="script">n</mi><mi mathvariant="script">a</mi></mrow></semantics></math></inline-formula> operations on the Fermatean fuzzy environment and Fermatean Fuzzy A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">c</mi><mi mathvariant="script">z</mi><mi mathvariant="script">e</mi><mi mathvariant="script">l</mi></mrow></semantics></math></inline-formula>–A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">l</mi><mi mathvariant="script">s</mi><mi mathvariant="script">i</mi><mi mathvariant="script">n</mi><mi mathvariant="script">a</mi></mrow></semantics></math></inline-formula> (FF-AA) average aggregation operators, including their properties such as idempotency, monotonicity, and boundedness. Further, we introduce a Fermatean fuzzy A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">c</mi><mi mathvariant="script">z</mi><mi mathvariant="script">e</mi><mi mathvariant="script">l</mi></mrow></semantics></math></inline-formula>–A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">l</mi><mi mathvariant="script">s</mi><mi mathvariant="script">i</mi><mi mathvariant="script">n</mi><mi mathvariant="script">a</mi></mrow></semantics></math></inline-formula> weighted average closeness coefficient (FF-AA-WA-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">C</mi><mi mathvariant="script">C</mi></mrow></semantics></math></inline-formula>) aggregation operator (AO) based on the closeness coefficient for MAGDM issues. By utilizing the proposed technique, we solve a numerical example of an MAGDM problem. The results show that this approach is accurate and practical, and consistent with a realistic investment circumstance. A demonstration was created to emphasize the significance and credibility of this approach and assess its validity by comparing its outcomes with the established methods.