Power transformers need to be continuously monitored in order to identify emerging issues before they become catastrophic failures. In order to assess transformer condition in real time, this paper proposes a fuzzy logic-based transformer health monitoring system that combines several sensor inputs (voltage, current, oil temperature, oil level, etc.). A Mamdani fuzzy inference engine is implemented by an ATmega328 microcontroller, which also uses GSM to notify maintenance staff of faults. To tune the fuzzy logic controller and simulate different fault scenarios (overloads, over-temperature, undervoltage, etc.), a comprehensive MATLAB/Simulink model was created. According to simulation results, the fuzzy system can identify the transformer's health status as Normal, Alert, or Critical and initiate the proper control measures (such as load reduction or emergency trip) well before serious faults occur. The behavior of a hardware prototype that was constructed and tested on a laboratory transformer closely matched the predictions of the simulation. When handling ambiguous sensor data and integrating several fault indicators into a single health index, the fuzzy logic approach demonstrates strong capabilities. In general, this work offers a thorough framework for fuzzy logic-based intelligent transformer condition monitoring, enhancing the sensitivity of fault detection and operational dependability of transformers