Abstract: The lightweight design of rail vehicle bogies effectively reduces overall energy consumption and enhances dynamic performance. Aluminum alloys, due to its lightweight and high-strength properties, have been widely utilized in the design of critical components. Arc Additive Manufacturing, characterized by its low cost and high efficiency, enables customized fabrication of components; however, its excessive thermal efficiency often results in reduced forming accuracy. This study addresses this issue through additive manufacturing experiments on 5356 aluminum alloy using three welding techniques: MIG-AM, DP-MIG-AM, and CMT-AM. The research investigates the influence of different combinations of three process parameters—stick-out length, welding current, and welding speed—on the macroscopic morphology of single-pass single-layer weld beads. Based on the macroscopic geometric data of single-pass single-layer welds, qualitative and quantitative relationships among parameters, quality, and forming characteristics are established. Furthermore, a data-driven machine learning approach is employed to predict the geometric morphology of weld beads, and a Particle Swarm Optimization (PSO) algorithm is integrated to optimize the model, enhancing its iteration efficiency and prediction accuracy. Finally, optimized process parameters for each welding technique are selected for single-pass multi-layer experiments, with evaluations conducted on deposition efficiency and mechanical properties.