This paper proposes an optimal tuning framework based on artificial neural networks for Laguerre function-based model predictive control using exponential data weighting (ANN-Laguerre-EDW MPC) applied to load frequency control (LFC) in renewable-integrated microgrids. The proposed approach eliminates the need for iterative optimization strategies by utilizing an ANN as a surrogate model to learn the relationship between controller settings and system performance. The approach is evaluated against conventional PID, MPC, Laguerre-EDW MPC, and PSO-based Laguerre-EDW MPC approaches under both step and random load perturbation conditions. Simulation results indicate that the proposed approach achieves up to a 68% reduction in the performance index (ISE) value relative to the other approaches, while facilitating smoother control actions and minimizing frequency oscillations. Moreover, the approach significantly decreases computing time by as much as 95% relative to PSO-based optimization, underscoring its computational efficiency and suitability for practical microgrid control implementation. The proposed LFC approach is further substantiated through sensitivity analysis against parametric uncertainty and statistical analysis, affirming its robustness and reliable performance.