In this study, a hybrid model for prediction issues based on IT2FLS and particle swarm optimization (PSO) is proposed. The main contribution of this paper is to discover the ideal strategy for creating an optimal value vector to optimize the membership function of the fuzzy controller. It should be emphasized that the optimized fuzzy controller is a type 2 interval fuzzy controller, which is better than a type 1 fuzzy controller in handling uncertainties. The type-2 fuzzy set domain's limiting membership functions are type-1 fuzzy sets, which explains the trace of uncertainty in this situation. The proposed optimization strategy was tested utilizing ECG signal data. The accuracy of the proposed IT2FLS PSO estimation technique was evaluated using a number of performance metrics (MSE, RMSE, Error Mean, Error STD). The simulation results show that the PSO is effective in designing optimal type 2 intermittent fuzzy controllers. The experimental results show that the proposed optimization strategy significantly improves the prediction accuracy.