In the current study, an experimental setup consisting of smoothbore 30-caliber powder gun was employed to launch spherical projectiles (3 g/ϕ9) in the ballistic range of velocities from 500 to 1700 m/s and obliquity (0, 33 and 65°), impacting 2 mm thin Steel 1006 target plates. Crater dimensions (major and minor dia.) obtained from series of FE simulations run for the above configuaration was validated by corresponding experimental data. Subsequently, a fullscale 3D FE model considering 8-noded hexahedral elements was used to discretize SS304 conical projectiles (replacing spherical projectile) with various apex angles viz. 40°, 60°, 80° and 100° impacting on single (3 mm) and double layer (1.5 mm each) steel 1006 targets (replacing 2 mm target) in LS dyna. The material behavior was characterized using J–C strength and damage models, along with Gruneisen Equation of State. An erosion algorithm was used in the explicit FE code LS-DYNA to remove undesirable elements. In the next stage, Adam and Nadam optimizers have been employed in ANN models developed using Python code within the Tensor-Flow framework. The Keras Tuner library in the Tensor-Flow framework was used for hyper parameter tuning. The ANN models trained using simulation data successfully predicted the residual KE of conical projectiles with intermediate apex angles of 90°, 70°, and 50° across twelve different impact scenarios. The models with Adam and Nadam optimizers achieved mean squared error (MSE)/coefficient of determination (R2)/mean absolute percentage error (MAPE) values of 109.44/0.998/6.72 and 91.19/0.998/7.76, respectively.