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The concept of the "Trojan Horse" originates from the closing days of the legendary Trojan War, a narrative solidified in the works of Homer and Virgil. In these classical texts, the Greek army utilized a strategy of intellectual subversion, constructing a benign gift to conceal a malicious payload, to bypass the static fortifications of Troy. In the contemporary digital landscape, this ancient stratagem mirrors the evolution of modern malware, where burgeoning complexity has neutralized the efficacy of conventional, signature-reliant defence paradigms. This study proposes a high-fidelity Intelligent Detection Framework designed to transcend static identification by leveraging the predictive power of ensemble learning. Utilizing a curated corpus of 4,000 observations, the operational pipeline employs Principal Component Analysis (PCA) to distill raw telemetry into the 22 most significant behavioral dimensions. Performance benchmarks reveal that while standard Decision Trees offer baseline competence, the Extreme Gradient Boosting (XGBoost) configuration attains a dominant 98.7% accuracy and a 99.2% recall. By fusing the historical logic of the Trojan ruse with high-performance gradient boosting, this research establishes a scalable blueprint for fortifying endpoint security against the next generation of stealth-oriented cyber threats.