The process of welding is of utmost importance in the manufacturing sector, as it is extensively utilized across diverse industries. The efficacy of welding plays a crucial role in guaranteeing the quality of products and their cost-efficiency. The investigation commences by constructing an all-encompassing mathematical model that effectively captures the intricacies of the welding process. The proposed model integrates many input parameters, such as welding current, voltage, speed, and gas flow rate, among other relevant factors. The utilization of a mathematical model facilitates a more profound comprehension of the intricate interconnections among these parameters and the resultant quality of the weld. The aforementioned tool plays a crucial role in the prediction of welding outcomes and the identification of areas that have potential for development. In order to enhance the efficiency of the welding process parameters, a methodology utilizing the SVMs approach is implemented. SVM are a very effective machine learning methodology renowned for their proficiency in handling nonlinear regression and classification problems. This study employs Support Vector Machines (SVM) to ascertain the most advantageous amalgamation of welding parameters, resulting in improved process efficiency and greater weld quality. The SVM optimization technique exhibits the ability to effectively manage difficult welding operations due to its capacity to handle complex and high-dimensional parameter spaces. Combining mathematical modeling and SVM-based parameter optimization, this method not only improves the quality of the weld, but it also cuts costs and has minimal effects on the environment. This study presents a structured approach for improving the efficiency of the welding process by utilizing mathematical modeling and SVM-based parameter optimization. The suggested methodology possesses the capacity to significantly transform welding methodologies across several sectors, fostering sustainability and cost-efficiency.