Maintaining an appropriate calorie intake based on a person’s age, height, weight, gender and other body vitals ensures the health and wellbeing of a person. The existing methods for calorie tracking in intake food has certain disadvantages which includes failure in individualization of food. The main aim of the proposed system is to accurately measure a person’s intake calories based on the food they consume and the amount of food taken. The proposed system is a smartwatch-based food tracking and monitoring system that precisely determines a user's calorie consumption from their meals using image processing and machine learning. The system comprises of a smartphone app for user interaction, a smart spoon for weighing food, and a wristwatch with a camera. The wristwatch takes a picture of the meal, which is then examined using image processing to determine the food's nature and nutritional composition. The electronic spoon weighs the meal and transmits the system with the weight data. The system then uses the food weight and nutritional values to compute the consumption of calories, and it subsequently records the results in a database. The database's data is analysed by the machine learning component to produce customised dietary recommendations for the user, including the number of calories that must be consumed or lost in accordance with their diet. Through the mobile app, the user may examine their daily dietary consumption, get advice, and establish objectives. The programme also features a notification system that alerts the user to recommendations and delivers reminders to track meals.