<p>Uncertainty and fuzziness are involved in almost all aspects of life. Multi-attribute decision-making is the most suitable technique for identifying the optimal alternative from uncertain and fuzzy information, thereby reducing ambiguity in practical applications. n the modern age, business has shifted from a physical to an online mode, exemplified by Stack Exchange. However, it is too technical for new businesspeople to find the right investment opportunity in the stock exchange market. For handling such types of problems, the Pythagorean hesitant fuzzy set framework is an advanced and flexible assessment tool. The Sugeno–Weber t-norm and Sugeno–Weber t-conorm operations provide a flexible environment for data aggregation. The thought is that the Pythagorean hesitant fuzzy set is a more flexible and advanced structure than the simple one due to the hesitant degree. By motivating the concept of Pythagorean hesitant fuzzy set and Sugeno–Weber operation, we developed a family of aggregation operators called Pythagorean hesitant fuzzy Sugeno–Weber weighted averaging and Pythagorean hesitant fuzzy Sugeno–Weber weighted geometric operators. Additionally, we examine some fundamental axioms of aggregation operators, including boundedness, monotonicity, and idempotency, to verify the authenticity and accuracy of the proposed theory. To construct a multi-attribute decision-making algorithm based on the developed theory. We offer a solution to real-life multi-attribute decision-making (MADM) problems involving money investments in the stock exchange market through our diagnostic approach. Compared with existing methodologies, we investigate the applicability of Pythagorean hesitant fuzzy Sugeno Weber weighted averaging and Pythagorean hesitant fuzzy Sugeno Weber weighted geometric operators. Solid conclusions are discussed in the last section.</p>