This study presents a comprehensive framework combining finite element analysis, machine learning, and generative AI for aluminum cold spray deposition analysis. Abaqus explicit dynamic simulations modeled high-velocity particle impact at 700 m/s, capturing stress tensor components and von Mises equivalent stress distributions. The maximum von Mises stress of 537.73 MPa exceeded aluminum yield strength by 3.6 times, confirming successful deposition through severe plastic deformation. Three machine learning algorithms were trained on stress tensor components (S11, S22, S33, S12, S13, S23) to predict von Mises stress. Random Forest, Gradient Boosting, and Neural Network models achieved exceptional accuracy with R² values of 0.9975, 0.9955, and 0.9922 respectively. Hyperparameter optimization further improved performance to R² = 0.9977, 0.9887, and 0.9985. Feature importance analysis identified S22 transverse stress as the dominant predictor with 80% importance. Google Gemini generative AI provided engineering insights confirming bonding mechanisms through adiabatic shear instability and oxide disruption. Process optimization recommendations addressed velocity control, particle distribution, and substrate preparation. This integrated approach enables rapid stress prediction and intelligent process optimization for industrial cold spray applications.