The daily nutrition management is one of the most important issues that concern individuals in the modern lifestyle. Over the years, the development of dietary assessment systems and applications based on food images has assisted experts to manage people's nutritional facts and eating habits. In these systems, the food volume estimation is the most important task for calculating food quantity and nutritional information. In this study, we present a novel methodology for food weight estimation based on a food image, using the Random Forest regression algorithm. The weight estimation model was trained on a unique dataset of 5,177 annotated Mediterranean food images, consisting of 50 different foods with a reference card placed next to the plate. Then, we created a data frame of 6,425 records from the annotated food images with features such as: food area, reference object area, food id, food category and food weight. Finally, using the Random Forest regression algorithm and applying nested cross validation and hyperparameters tuning, we trained the weight estimation model. The proposed model achieves 22.6 grams average difference between predicted and real weight values for each food item record in the data frame and 15.1% mean absolute percentage error for each food item, opening new perspectives in food image-based volume and nutrition estimation models and systems.Clinical Relevance- The proposed methodology is suitable for healthcare systems and applications that monitor an individual's malnutrition, offering the ability to estimate the energy and nutrients consumed using an image of the meal.