This paper proposes an intelligent optimization method based on Gaussian process regression (GPR) surrogate model and particle swarm optimization (PSO) algorithm for the heat dissipation optimization of finned liquid cooling plates. First, a sample dataset of fin angles, temperatures, and pressures is constructed through COMSOL simulations, and the GPR surrogate model is trained to establish a high-precision response surface. On this basis, the PSO algorithm is used to perform global optimization within the feasible range of fin angles, with the optimization objectives of simultaneously reducing the bottom wall temperature of the liquid cooling plate and the internal flow pressure. This method can efficiently determine the optimal fin angle. The entire process uses only coarse mesh data to train the model, and the average relative errors of the peak temperature and pressure are 0.07% and 5.36%, respectively. At a flow rate of 500 mL/min, the peak temperature of the liquid cooling plate after fin angle optimization is reduced by approximately 8.94 K. This method significantly reduces the dependence of the optimization process on computing resources and provides an effective approach for the rapid design optimization of finned liquid cooling plates.