This study explores the potential of Explainable Artificial Intelligence (XAI) algorithms, with a particular focus on the SHapley Additive Explanations algorithm (SHAP), for providing novel insights into the complex dynamics of turbulence. Turbulence is a highly non-linear and chaotic phenomenon, which presents significant challenges in terms of prediction and modelling. It is often the case that traditional methods are unable to adequately capture the intricate details of turbulent flows, thereby necessitating the exploration of advanced computational techniques. In this study, we utilise convolutional neural networks (CNNs) in conjunction with the SHAP algorithm to analyse and predict turbulent flow patterns in an axisymmetric jet.