Laser Beam Welding (LBW) is one of the sophisticated welding processes that find applications in multiple technical disciplines such as vehicle manufacturing, aviation, and electronics industries. For establishing sound weld joints, it is vital to improve artificial intelligence tools effectively and affordably. In this current inquiry, a hybrid optimization artificial intelligence tool has been built for selecting the best process parameters for getting improved multi-performance features. The tests were prepared by employing Taguchi’s design technique by considering several independent factors such as Laser Power (LP), Weld Speed (WS) and Pulse Duration (PD). Experiments on Inconel 718 alloy thin sheets have been conducted to optimise the required output measurements. The process parameters were optimised using the Grey-ANFIS-based Jaya algorithm to achieve the smallest top/bottom width and greatest penetration. Grey theory has been applied for establishing the multi-performance index. The performance of the created optimization model is assessed through statistical error analysis. From the investigational findings, the best-suited process variables combinations have been established which will be applied for reaching superior and enhanced multi-performance features.