<p>Machine learning algorithms have modernised the field of aerodynamics by providing innovative solutions to complex problems by optimizing design parameters, create surrogate model, and deep learning network in flow prediction and analysis. This research paper examines the combination of thin airfoil theory (TAT), the vortex panel method, open-source experimental data, computational fluid dynamics (CFD) simulations, and convulational neural networks (CNNs) to characterise the airfoil aerodynamics of NACA4412 airfoil. Through analysis and experimentation, the study demonstrates that significant computational efficiency gains are achieved by employing CNNs compared to traditional CFD methods, with a calculated efficiency increase of approximately 4.74 times. Furthermore, the accuracy of the developed CNN model increases with increasing training epochs, reaching an accuracy of 83.09% after 100 epochs. By utilising the trained CNN model, this research highlights the potential for future airfoil optimization prospects, offering opportunities to enhance performance and efficiency in various engineering applications. The refinement of open-source experimental data, coupled with the utilisation of TAT and the vortex panel method, contributes to a comprehensive understanding of airfoil aerodynamics. The present study substantially contributes to the literature by providing useful insights into the synergistic integration of machine learning approaches with classical aerodynamic analytic methods to help advance aerofoil analysis.</p>