Abstract: High-entropy alloys (HEAs) are promising for structural applications due to their superior mechanical and corrosion properties. Six machine learning (ML) algorithms were evaluated for predicting phase formation, with the random forest classifier (RFC) achieving the highest accuracy (87%) and receiver operating characteristic-area under curve (ROC-AUC) score (0.954). Using the optimized ML framework, Fe_(25−X)Co₂₅Ni₂₅Cr₂₀V₅Ta_X (X = 2.5at%–10at%) HEAs were designed and fabricated. Metallurgical characterization confirmed a eutectic phase equilibrium with increased lattice distortion at higher Ta contents. Nanoindentation revealed hardness from 2.655–5.083 GPa and modulus from 212.71–286.84 GPa. Scratch testing showed an initial increase in the coefficient of friction, followed by transient behavior and subsequent stabilization, consistent with the progressive evolution of the 3D wear-surface morphology. The close agreement between ML predictions and experimental results demonstrates the effectiveness of data-driven alloy design for tailoring microstructure and optimizing mechanical performance in advanced HEAs.