This study introduces a novel deep learning-based technique for predicting pressure distribution images, aimed at application in image-based approximate optimal design. The proposed approach integrates both unsupervised and supervised learning paradigms, employing autoencoders (AE) for the unsupervised component and fully connected neural networks (FNN) for the supervised component. A surrogate model based on 2D image data was developed, enabling a comparative analysis of three distinct methods: the conventional AE, the convolutional autoencoder (CAE), and a hybrid CAE, which combines the CAE with a conventional AE. Extensive experiments demonstrated that the CAE method achieved the highest learning capability and restoration rate for pressure distribution images of 2D airfoils. The compressed latent image data were utilized as inputs for the FNN, which was trained to predict latent features. These features were decoded to forecast the corresponding pressure distribution images. The results showed excellent concordance with those derived from computational fluid dynamics (CFD) simulations, achieving a match rate exceeding 99.99%. This methodology significantly simplifies and accelerates image prediction, rendering it feasible without requiring specialized CFD knowledge. Moreover, it enhances accuracy while streamlining the neural network structure. Consequently, it provides foundational technology for image data-based optimization, establishing a platform for future AI-driven design and optimization advancements.