Blockchain networks utilize same security checks for every transaction, without considering the level of risk of the transaction. Our framework suggests adding fuzzy logic to the already existing consensus protocols for enabling adaptive and context-sensitive validation. Our Mamdani fuzzy system utilizes four inputs which include transaction, sender reputation, network congestion and temporal patterns. Adding along sixteen rules to compute security levels, hence dynamically adapting to validations across three tiers. We prove safety preservation with exponential bound \(P_{\text {unsafe}} \le (f/k)^k e^{-\gamma s_l}\) , demonstrate O(1) fuzzy inference overhead, and establish information-theoretic entropy gain of \(\log _2 3 \approx 1.58\) bits over static systems. The framework maintains Byzantine fault tolerance while enabling risk-proportional resource allocation.