Failure mode and effects analysis (FMEA) is a proactive method used to identify and prevent potential failures in processes or products. However, this traditional approach often has limitations. To address these, a new fuzzy risk-based approach is introduced in this study, enhancing the FMEA methodology. This model replaces the conventional criticality calculation with a fuzzy inference technique. Fuzzy logic is applied, using membership functions to evaluate risk, rank failure modes, and prioritize measures to mitigate the risks of undesirable events. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a dynamic and intelligent model to enhance and verify the results obtained from the fuzzy inference system, effectively predicting the criticality of failure modes. The application of this model is demonstrated through a case study within the electric vehicles system, showcasing its potential. This analysis provides a different ranking of failure modes and improves decision-making by offering a preventive-corrective plan.