Robotic welding systems require precise pre-programming of key parameters, such as weld initiation and termination points, seam geometry, welding speed, and voltage, to ensure high-quality and consistent welds. However, defining welding points and seam profiles is a labor-intensive process that demands careful manual input, making it prone to alignment errors. These errors can negatively impact both the precision and efficiency of the welding process, potentially leading to defects in the final weld. Additionally, manual parameter adjustments introduce variability due to human error, causing inconsistencies in weld quality and reducing overall process reliability. Consequently, there is a growing demand for advanced automation solutions to improve accuracy, minimize human intervention, and enhance the efficiency of robotic welding systems in industrial applications. To address these challenges, this study proposes an innovative algorithm for automated weld path determination using a 2D laser sensor. The algorithm commences with data acquisition, wherein the sensor captures the weld seam profile. This data is subsequently processed through a mathematical model to compute the optimal welding trajectory. An experimental setup is developed, integrating a Programmable Logic Controller as the central control unit, a 2D laser sensor for seam detection, and an industrial welding robot. Real-world testing is conducted, with data collection and analysis employed to assess the system’s accuracy and effectiveness. Experimental results validate the proposed algorithm, demonstrating enhanced precision, reliability, and adaptability in robotic welding applications.