Deep learning models offer state-of-the-art performance but their inherent opacity is a major barrier to adoption in high-stakes domains. In contrast, Takagi-Sugeno-Kang (TSK) fuzzy systems provide rule-based transparency but often lack the predictive power of deep networks. My PhD research addresses this critical trade-off by developing the Fuzzy-Modulated Linear Consequents (FMLC) framework, a novel hybrid architecture that synergizes these two paradigms. The core of FMLC is a deep neural network that processes fuzzified input features to generate context-dependent "modulators". These modulators dynamically parameterize a TSK-style linear consequent layer, creating a model that is both highly performant and inherently interpretable. My latest work, Learnable-FMLC (L-FMLC), advances this by introducing a regularized, adaptive fuzzification layer that autonomously learns the optimal fuzzy partitions from data, and a two-stage rule distillation framework to ensure interpretability remains scalable in high-dimensional problems. This research delivers a validated, theoretically-grounded, and scalable framework, contributing a significant step towards transparent and trustworthy AI.