This thesis explores the integration of machine learning and computer vision technologies to automate calorie intake tracking, aiming to revolutionize traditional methods by leveraging object detection techniques. The study focuses on training and evaluating different versions of YOLO object detection model (YOLOv5, YOLOv6, YOLOv7 and YOLOv8) on a custom food dataset. Through experiments and simulated testing, the performance of these models is analyzed in terms of accuracy, speed, and practical usability. Additionally, a FastApi web application is developed to provide a user-friendly interface for capturing and uploading food images, processing them with the object detection model, and storing the daily calorie intake. The results demonstrate the potential of machine learning-driven solutions in addressing the challenges of manual calorie tracking, paving the way for more efficient and accurate dietary management systems. Results indicate that YOLOv5 and YOLOv6 exhibited similar performance, offering a balance between accuracy and speed, with YOLOv6 demonstrating slightly better accuracy. In contrast, YOLOv7-tiny, trained with a smaller image size, showed a significant drop in recall and F1 scores compared to its predecessors. Conversely, YOLOv8 achieved the highest accuracy among the tested models due to architecture improvements and optimizations.