Fuzzy Cognitive Maps (FCMs) have emerged as powerful tools for addressing diverse
engineering challenges, leveraging their cognitive nature and ability to encapsulate
causal relationships. This paper provides a comprehensive review of FCM applications
across 15 engineering sub-domains, categorizing 80 studies by their learning family,
task type, and case-specific application. We analyze the methodological advancements
and practical implementations of FCMs, showcasing their strengths in areas such as
decision-making, classification, time-series, diagnosis, and optimization. Qualitative
criteria are systematically applied to classify FCM-based methodologies, highlighting
trends, practical implications of varying complexity, and human intervention across
task types and learning families. However, this study also identifies key limitations,
including scalability challenges, reliance on expert knowledge, and sensitivity to
data distribution shifts in real-world settings. To address these issues, we outline
key areas and directions for future research focusing on adaptive learning mechanisms,
hybrid methodologies, and scalable computational frameworks to enhance FCM performance
in dynamic and evolving contexts. The findings of this review offer a structured roadmap
for advancing FCM methodologies and broadening their application scope in both contemporary
and emerging engineering domains.
