<p>Cancer remains one of the most critical worldwide health challenges, with incidence and mortality rates projected to increase significantly. While mathematical models have played an instrumental role in medicine—from disease identification to drug discovery—the application of fuzzy neural networks (FNN) in cancer research represents a promising yet underexplored frontier. To address this gap, this systematic review aims to evaluate the current state of neuro-fuzzy systems in oncology, analyze their methodological limitations, and outline future pathways for real world application in clinical decisions. Specifically, this review examines the application of these systems in cancer detection, classification, and diagnosis across five major cancer types: breast, colorectal, lung, prostate, and stomach cancers. Analyzing literature from 2014 to 2025, we evaluate a spectrum of architectures ranging from highly interpretable rule based expert systems to complex fuzzy-deep learning hybrids. A comprehensive examination reveals that hybrid architectures combining deep learning with fuzzy systems achieve accuracies ranging from 86% to 100% for cancer classification tasks. However, despite these promising computational metrics, critical barriers currently prevent their translation into routine clinical practice. Specifically, the “black-box” nature of advanced models undermines physician trust, while limited dataset diversity and non-standardized validation protocols severely restrict the models’ ability to generalize across diverse patient populations. This review identifies these practical bottlenecks and provides recommendations for advancing FNN applications in oncology. By emphasizing the need for robust, multi-center datasets and explicit algorithmic transparency, this study charts a course for transforming FNNs from theoretical models into reliable, explainable decision-support tools that directly enhance clinical diagnostic workflows and personalized patient care.</p>