In later a long time, progressions in counterfeit insights and computer vision have empowered computerizedarrangements for wellbeing observing and dietary evaluation. In this article, a deep learning-based system forestimating food calories is presented with the help of object recognition and image processing. The proposedsystem uses the Yolov5 model for food detection, OpenCV for the preprocessing of the interactive user interface,and power supplies. By using pre-formed models and pre-defined calorie data sets, the system recognizes foodand provides approximate calorie counts based on volume estimation techniques. This paper presents aprofound learning-based framework for evaluating nourishment calories utilizing question location and picturehandling. The proposed framework utilizes the YOLOv5 demonstrate for nourishment acknowledgment,OpenCV for picture preprocessing, and Stream lit for an intelligently client interface. The comes aboutdemonstrate that this approach is productive and adaptable for real-time applications in individual wellbeingtracking