Optimal sensor placement is a crucial issue in scientific and engineering research. This study proposes an autoencoder-based deep learning framework for automated optimal sensor layout and flow field reconstruction. A dataset is established based on transient Computational Fluid Dynamics (CFD) simulation results of a three-dimensional finite-span airfoil under various Reynolds numbers and angles of attack, enabling high-precision reconstruction of airfoil surface pressure distribution using sparse pressure coefficient data. The multilayer perceptron of both the encoder and decoder adopts an optimal five-layer structure with 200 nodes per layer, and the ReLU activation function delivers superior performance with a training loss reduction of over 45%. When using 50 sensors, the proposed architecture determined detailed placement and obtained a reconstruction error of 0.0604, which outperforms traditional manual sensor placement. The reconstruction accuracy of aerodynamic loads improves with increasing sensor count, but exhibits diminishing returns beyond the optimal threshold 50, necessitating a balanced selection that optimizes performance-to-cost ratio. The proposed method adaptively captures critical flow regions with high gradients, cutting sensor quantity by 60–80% versus grid-based placement. This method can flexibly use either CFD or experimental data in practical applications, offering an efficient solution for aerodynamic field reconstruction.