Adaptive impedance matching between antennas and radio frequency front-end (RFFE) power modules is essential for mobile communication systems. To address the matching performance degradation caused by parasitic effects in practical tunable matching networks (TMNs), this paper proposes a data-driven adaptive impedance matching method that avoids physical adjustment. First, we propose the residual enhanced circuit behavior modeling network (RECBM-Net), a deep learning model that maps TMN operating states to their scattering parameters (S-parameters). Then, we formulate the matching process based on the trained surrogate model as a mathematical optimization problem. We employ two classic numerical methods with different online computational overhead, namely simulated annealing particle swarm optimization (SAPSO) and adaptive moment estimation with automatic differentiation (AD-Adam), to search for the matching solution. To further reduce the online inference overhead caused by repeated forward propagation through RECBM-Net, we train an inverse mapping solver network (IMS-Net) to directly predict the optimal solution. Simulation results show that RECBM-Net accurately predicts S-parameters, achieving a mean absolute error of $6.98 \times 10^{-5}$. Across 9000 mismatched scenarios, the compliance rate after tuning increases from 0.97% with the analytical solution of the ideal L-network to 95.92% with SAPSO, 93.42% with AD-Adam, and 95% with IMS-Net. While AD-Adam significantly reduces computational overhead, lowering the average number of RECBM-Net inferences from 2097 with SAPSO to 285, it sacrifices some accuracy. IMS-Net requires only a single inference to obtain the matching solution, resulting in minimal online overhead while maintaining excellent matching accuracy.