Fish is commonly acknowledged as a highly nutritious food in many regions worldwide, and humans have been consuming fish for centuries to meet their protein and nutritional requirements. The consumption of fresh fish offers numerous benefits, as they contain essential proteins and materials that may be challenging to obtain from alternative sources. However, the freshness of fish decreases after a few days. Humans can determine the freshness of fish by looking at its eyes, smelling it, and checking its gills. But, can machines do the same? This study proposes a novel approach to evaluate the freshness of fish using deep learning techniques. Despite the long-standing tradition of humans determining fish freshness by sensory analysis, the objective evaluation of fish freshness has been challenging. By employing deep learning algorithms (SqueezeNet and InceptionV3) to classify fish based on their freshness using a dataset of 4476 images of fish bodies categorized as fresh and stale, this study provides a new method to address this challenge. Analyzing the results of the study revealed that the SVM, ANN, and LR models result in an accuracy rate of 100% for each deep learning method. This outcome indicates a greater percentage than the previous research, which was 98.0%. This research's novelty lies in its application of deep learning techniques to determine fish freshness objectively, providing a reliable and cost-effective method to evaluate fish freshness. The significance of this study lies in its potential applications in the food industry, offering a reliable method for quality control and food safety.