<p>The fourth industrial revolution heavily relies on intelligent manufacturing, which accurately and robotically manages mechanical appliances. There is potential to improve the efficiency of manufacturing facilities. Still, defects and possible mishaps in the production process affect the workflow, deplete resources, and worsen environmental effects. Failure modes and effects analysis (FMEA) is a systematic method for identifying, analyzing, and removing potential failures in products, designs, and procedures. Due to the uncertainty of multiple natures, more than one method or technique is needed to deal with such flaws or failures. Ultimately, there is a dire need to develop hybrid models to address and resolve manufacturing process failures. Many fuzzy MCDM techniques have designed to deal with and quantify uncertainty when assessing failure modes; these methods often use fractional fuzzy numbers and FMEA. When modeling complex and asymmetric fuzzy sets, the (p, q)-fractional fuzzy numbers offer a more expressive and accurate alternative to the more basic and limited fractional fuzzy numbers. This paper first develops Aczel-Alsina averaging operators in (p, q)-fractional fuzzy context, along with some of their basic properties. After that, we proposed a novel approach to prioritize FMEA risks by combining (p, q)-fractional fuzzy numbers with MABAC and TOPSIS methods to address ambiguity in expert opinions. Furthermore, we demonstrate that a practical case study of robots employed in the cabal industry can have their potential failures identified and assessed more effectively with the help of the suggested (p, q)-fractional fuzzy techniques. Finally, we performed a comparison of the deduced work with the prevailing research to validate the supremacy and sensitivity analysis to illustrate the persistence of parameters on decision outputs.</p>