Understanding food classes and its numerous blends has been an interesting area of research as more people become more health conscious of not only how appetizing and savory a meal may appear to be, but also its nutritional content. With the advent of social media and ever-increasing availability of data in this regard, there has been extensive research and sophisticated computational components, models, and methodologies developed to leverage this. This research paper is mostly driven to contribute to the wealth of knowledge already obtained in this area by looking to use pretrained models to reliably predict or label food images. It is research heavily reliant on comparing the performance of three models namely, Alexnet, ResNet18, and Vgg16 in food image classification. Upon training, validating, and testing all three models, Resnet18 ranked the topmost performing model based on an accuracy of AQ1 approximately 88.4% on unseen data while also achieving the least loss values which were based on the evaluation of ground truth labels as against the predicted labels.