—Image recognition has emerged as a vital area in image processing and computer vision. Among its various applications, food image classification stands out due to the increasing awareness of health and dietary management. Many individuals utilize dietary assessment systems that leverage food image classification to monitor their dietary habits. However, classifying food images presents significant challenges due to the non-linear nature of food datasets. This paper introduces a novel method for food image classification employing advanced neural network models. Specifically, we utilized state-of-the-art convolutional neural networks (CNNs) pre trained on comprehensive datasets. Our experiments involved the Food-11 dataset, where we achieved an impressive accuracy of 96.75%