A data-driven framework is presented that extends the predictive capability of classical lifting-line theory (LLT) to a wider aerodynamic regime by incorporating higher-fidelity aerodynamic data from panel method simulations. A neural network architecture with a convolutional layer followed by fully connected layers was developed, comprising two parallel subnetworks to separately process spanwise collocation points and global geometric/aerodynamic inputs, such as angle of attack, chord, twist, airfoil distribution, and sweep. Among several configurations tested, this architecture proved most effective in learning corrections to LLT outputs. The trained model captured higher-order three-dimensional effects in spanwise lift and drag distributions in regimes in which LLT is inaccurate, such as low aspect ratios and high sweep, and generalized well to wing configurations outside both the LLT regime and the training-data range. The method retained the computational efficiency of LLT, enabling integration into aerodynamic optimization loops and early-stage aircraft design studies. This approach offers a practical path for embedding high-fidelity corrections into low-order methods and may be extended to other aerodynamic prediction tasks, such as propeller performance.