Commercial ductile steel sheet is widely used in engineering applications because of its favorable combination of strength, ductility, machinability, and mechanical versatility. However, its structural integrity is frequently compromised by geometric discontinuities, specifically notches. These stress raisers negatively affect fracture toughness and fatigue strength. The V-notch is particularly prevalent and critical in structural failure analysis. This study presents an experimental, numerical, and machine-learning investigation of the tensile properties of ductile steel sheet, focusing on various V-notch geometries. A systematic experimental study of 36 distinct specimens isolated the effects of geometric variations. Tensile tests were conducted in accordance with ASTM E8 standards to ensure reliability. Experimental results quantified tensile property degradation due to stress concentration, revealing severely compromised ductility in notched specimens. A validated numerical method subsequently captured necking and fracture behavior, reducing the need for expensive destructive experiments. Additionally, experimental data established a predictive framework for untested geometries. Random Forest, LightGBM, CatBoost, and XGBoost ensemble algorithms were employed for training and validation. Engineered features and hyperparameter tuning trained the models to effectively capture the nonlinear stress–strain region. CatBoost and LightGBM (R² = 0.9917 and 0.9902, respectively) provided the most stable predictive performance. This study establishes a validated baseline for V-notch behavior while demonstrating the effectiveness of data-driven models in minimizing extensive destructive testing.