To address the challenges of inner contour priority constraints, multi-component nesting, and suboptimal local convergence and efficiency in existing artificial immune algorithms, this study proposes a laser cutting path optimization method combining multi-objective clustering and adaptive artificial immunity. This method redefines the path optimization problem as a generalized traveling salesman problem with priority constraints. After establishing the relationships among multiple nested components, a multi-objective clustering algorithm is utilized to optimize the objective function and enhance the initialization and clone proliferation of the antibody population. During the crossover and mutation processes of the artificial immunity algorithm, self-cycling crossover and adaptive mutation operators are introduced to further refine the optimization. Experimental results indicate that the proposed algorithm achieves an average error of 0.24% compared to the optimal solution for generalized traveler datasets. In laser cutting tests, the optimized algorithm reduces convergence iterations by 67.68% compared to the standard artificial immune algorithm. Additionally, the null shift path is decreased by 10.31%, 8.21%, and 4.81% respectively compared to the artificial immunity algorithm, the artificial immunity-ant colony hybrid algorithm, and the immunity particle swarm algorithm, significantly enhancing laser cutting efficiency.