<p>Modelling spans various scientific fields, including statistics, mathematics, machine learning, artificial intelligence, deep learning, engineering, and bioinformatics. This interdisciplinary nature necessitates the creation of simple representations for modelling results. Performance measures play a crucial role in evaluating these models, indicating their effectiveness through scalar values, ratios, vectors, diagrams, or more complex indicators. These measures serve as the identities of models, highlighting their strengths and weaknesses. Thus, selecting the most appropriate performance measure is pivotal in model identification and comparison. Performance evaluation aids in selecting the optimal model from a pool of candidates, a task fraught with challenges due to the plethora of performance criteria available. Determining the best model becomes even more daunting given the diverse analysis methods and datasets. In this study, we propose a novel intelligent model evaluation method inspired by human intelligence, offering a fresh perspective on model assessment. The Intelligent Performance Measure (IPM) utilizes Convolutional Neural Networks. Unlike widely used performance measures in the literature, IPM evaluates the performance of models from a distinct perspective by learning the actual results in the form of images with all properties, rather than merely assessing error distance. The introduction, evaluation, and comparison of IPM have been thoroughly discussed, demonstrating that the proposed method is superior and represents a breakthrough in the field by being based on artificial intelligence. </p>