Model-assisted probability of detection (MAPoD) and sensitivity analysis are important to quantify the detection capabilities of eddy current nondestructive testing (ECNDT) systems. Due to the propagation of uncertainties in the MAPoD and SA problems of eddy current NDT, the traditional methods which are based on experiment and physical simulation models require a lot of time and labor costs. To reduce these costs, in this paper, the particle swarm optimization (PSO) algorithm optimized support vector regression (SVR) model is proposed to replace the traditional experiments and physical simulation models to predict the response of eddy current NDT models, thereby accelerating the analysis of MAPoD and SA problems. In addition, to the novelty, this paper combines the hyperparameter optimization algorithms such as grid search, random search, simulated annealing algorithm and PSO with SVR to test the accuracy and efficiency of them for the optimization of key parameters, and verify the advantages of PSO-SVR over other optimization algorithms based SVR. Finally, the PSO-SVR model is applied to the ECNDT problem, and the uncertainties in length of the surface slot is studied in MAPoD and SA analysis. The results show that the proposed method not only ensures the accuracy, but also accelerates the study for the MAPoD and SA analysis of eddy current NDT systems. It also reduces the computational costs, which accounts for 3.5% and 0.06% of those of the pure physical model in average.