The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.