This study presents the development and implementation of a comprehensive simulation model for assessing the technical condition of power autotransformers based on the physicochemical characteristics of transformer oil. The model integrates multiple diagnostic indicators such as acid number, moisture content, dielectric dissipation factor, and interfacial tension, which are key parameters reflecting the insulation aging process and overall oil degradation. Using the Fuzzy Logic Toolbox in MATLAB, the model applies a set of “IF–THEN” inference rules that describe the nonlinear and interdependent relationships between these diagnostic parameters and the Health Index (HI) of the autotransformer. This intelligent system enables the transformation of uncertain and imprecise input data into clear diagnostic conclusions, thus improving decision-making in transformer condition monitoring. The developed approach allows for continuous evaluation of insulation deterioration, thermal stress, and contamination, providing early detection of potential faults and supporting predictive maintenance strategies. Overall, the model serves as a valuable tool for enhancing the reliability, safety, and operational efficiency of power autotransformers in modern electric power systems