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The advent of smart grids and Intelligent Control Systems (ICS) has significantly enhanced efficiency and quality in power generation and distribution. Despite the large-scale benefits of these innovations, their interconnected nature exposes power systems to increasing cyber threats, including complex and persistent Man-in-the-Middle (MITM) attacks, which can inflict severe disruption in the power supply chain. To this extent, this study proposes a Blended Ensemble learning model, utilizing tree-based algorithms to detect and classify intricate MITM attacks effectively. Using the unique characteristics of its base classifiers, the model enhances performance, minimizes overfitting, and reduces latency. Evaluated on a real-world power system attack dataset, the model demonstrates superior accuracy in classifying MITM attacks and detecting subtle variants compared to traditional tree-based algorithms, highlighting its …