**Summary:** This paper proposes a gradient-based manifold meta learning (GMML) method for reconfigurable intelligent surface (RIS)-aided communications. The main challenge in RIS-aided systems is the joint optimization of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements, which is highly non-convex and computationally intensive. Traditional optimization methods suffer from high complexity, while deep learning-based methods lack robustness in dynamic environments. To address these issues, the authors introduce GMML, which combines meta learning and manifold learning to improve spectral efficiency and reduce computational overhead. GMML works without pre-training and uses gradients of the precoding and phase shifting matrices as inputs to neural networks, rather than direct channel state information (CSI). A differential regulator is designed to constrain the phase shifting matrix of the RIS. The method also reduces the search space by optimizing the precoding matrix on a low-dimensional manifold, which decreases convergence time and computational complexity. Numerical results show that GMML improves spectral efficiency by up to 7.31% and converges 23 times faster than traditional methods. It also demonstrates strong robustness and adaptability in dynamic scenarios. The proposed framework is pre-training free, has low computational overhead, and is effective in both perfect and imperfect CSI setups. The method is validated through simulations, showing superior performance compared to baseline approaches, including random phase, gradient-based meta learning (GML), meta learning (ML), deep neural networks (DNN), and alternating optimization (AO). GMML outperforms these methods in terms of spectral efficiency, convergence speed, and robustness to channel estimation errors. The results also highlight the effectiveness of GMML in dynamic scenarios, where it adapts well to changing environments. The paper concludes that GMML is a promising approach for RIS-aided communications, particularly in scenarios with imperfect CSI or no CSI.**Summary:** This paper proposes a gradient-based manifold meta learning (GMML) method for reconfigurable intelligent surface (RIS)-aided communications. The main challenge in RIS-aided systems is the joint optimization of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements, which is highly non-convex and computationally intensive. Traditional optimization methods suffer from high complexity, while deep learning-based methods lack robustness in dynamic environments. To address these issues, the authors introduce GMML, which combines meta learning and manifold learning to improve spectral efficiency and reduce computational overhead. GMML works without pre-training and uses gradients of the precoding and phase shifting matrices as inputs to neural networks, rather than direct channel state information (CSI). A differential regulator is designed to constrain the phase shifting matrix of the RIS. The method also reduces the search space by optimizing the precoding matrix on a low-dimensional manifold, which decreases convergence time and computational complexity. Numerical results show that GMML improves spectral efficiency by up to 7.31% and converges 23 times faster than traditional methods. It also demonstrates strong robustness and adaptability in dynamic scenarios. The proposed framework is pre-training free, has low computational overhead, and is effective in both perfect and imperfect CSI setups. The method is validated through simulations, showing superior performance compared to baseline approaches, including random phase, gradient-based meta learning (GML), meta learning (ML), deep neural networks (DNN), and alternating optimization (AO). GMML outperforms these methods in terms of spectral efficiency, convergence speed, and robustness to channel estimation errors. The results also highlight the effectiveness of GMML in dynamic scenarios, where it adapts well to changing environments. The paper concludes that GMML is a promising approach for RIS-aided communications, particularly in scenarios with imperfect CSI or no CSI.