One of the biggest problems in computer vision and nutritional science is accurately calculating the number of calories in food from pictures. In order to encourage better eating practices, this study tackles the challenge of automatically identifying food items in photos and supplying the relevant nutritional data, especially calorie levels. The YOLO (You Only Look Once) algorithm is used in the suggested system for effective and real-time food item detection. Its high accuracy and speed allow it to recognize several objects in a single image. A pre-existing nutritional dataset is then mapped to the detected food items, providing calorie information based on the meal type and approximate portion size. This method is unusual because it combines YOLO for food detection with a calorie estimating pipeline that is intended to function flawlessly in real-time via an intuitive online interface. This system, in contrast to conventional approaches, provides a fair balance between speed and accuracy, which makes it appropriate for real-time applications in diet planning, exercise, and healthcare. According to experimental results, the system is resilient in identifying a variety of foods and providing accurate calorie estimates, while also significantly improving processing efficiency when compared to traditional approaches. This study opens the door for further advancements in food recognition and dietary analysis while offering insightful information about developing automated nutritional evaluation systems.