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Abstract: A metasurface-loaded 1 × 2 patch array antenna assisted by a deep-learning optimization method is proposed to realize port and radiation pattern decoupling simultaneously for enhancing the isolation among elements in multi-input multi-output (MIMO) systems. The deep-learning-assisted optimization method uses an artificial neural network (ANN) and a particle swarm optimization (PSO) algorithm to seek the optimal structure of the antenna to achieve port decoupling with undistorted radiation patterns. The ANN is trained to describe the nonlinear relationship between the geometric parameters and the responses of the antenna. The PSO algorithm, guided by the cost function and number of iterations, is used to optimize the structure of the antenna according to the cost function combined with the trained ANN. Finally, by constraining the cost function, we obtained a 1 × 2 patch array antenna with a metasurface above fixed by studs achieving port and radiation pattern decoupling simultaneously. To validate the principle and design method, we designed fabricated, and measured a prototype with dimensions of 0.88λ₀ × 0.47λ₀ × 0.21λ₀ (λ₀ is the wavelength in free space at the center frequency). The measured fractional bandwidth was 8% (4.8 GHz ~ 5.2 GHz). The isolation of the two-element patch antenna increased from 7.6 to 24.3 dB with an envelope correlation coefficient (ECC) of < 0.0005 at 0.035λ₀. Moreover, the H-plane radiation pattern of each element was consistent and symmetric about the broadside direction. These characteristics make the proposed antenna suitable for MIMO antenna systems with close spacing.