Cancer is the leading cause of death in the world, with lung cancer having the greatest mortality rates since 1985. Recognizing with higher accuracy and predicting the type of Lung Cancer at the earliest possible stage will help patients have a better chance of surviving CNN has a major advantage that it can learn different features of the CT scan images for lung cancer. Within CNN, Resnet-50 transfer learning model could be used as a perfect replacement for the currently available algorithms that are used for the Lung Cancer Detection System as it has a key advantage that it is easy to optimise and hence enhances accuracy by adding more layers. ResNet-50 when used for the detection of lung cancer provided an accuracy of 66.92%. ResNet-50 comes with a variety of different medical systems such as Breast Cancer Detection with 99.10% accuracy, COVID-19 detection with 96.23% accuracy, Poultry Disease Recognition with 93.56% accuracy. This high accuracy of the ResNet-50 in various systems encouraged us to use it for the prediction of Lung Cancer which might increase the accuracy of prediction to approximately 95%. Hence, for this research we will be focussing on ResNet-50 for the diagnosis and prediction of Lung Cancer using Lung CT Scan Images.