Food image classification and recognition is an emerging research area due to its growing importance in the medical and health industries. As India is growing digitally rapidly, an automated Indian food image recognition system will help in the development of diet tracking, calorie estimation, and many other health and food consumption-related applications. In recent years many deep learning techniques evolved. Deep learning is a robust and low-cost method for extracting information from food images, though, challenges lie in extracting information from real-world food images due to various factors affecting image quality such as photos from different angles and positions, several objects appearing in the photo, etc. In this paper, we use CNN as our base model to build our system, which gives an accuracy of 86% to 89% of the system. After that, we deployed the transfer learning technique with MobileNetV3 for improvement in accuracy, which resulted in an improvement in accuracy of up to 94%. Furthermore, we applied data augmentation techniques in pre-processing phase and we train our model using transfer learning with MobileNetV3 and we got an accuracy of up to 96%. So, the accuracy of the model increases by applying the data augmentation technique on top of transfer learning.