Obesity is a growing problem globally. Currently 2.3 billion adults are overweight, and this number is rising. The most common method for weight loss is calorie counting, in which to lose weight a person should be in a calorie deficit. Basal Metabolic Rate accounts for the majority of calories a person burns in a day and it is therefore a major contributor to accurate calorie counting. This paper uses a Dynamic Bayesian Network to estimate Basal Metabolic Rate (BMR) for a sample of 219 individuals from all Body Mass Index (BMI) categories. The data was collected through the Lifesum app. A comparison of the estimated BMR values was made with the commonly used Harris Benedict equation, finding that food journaling is a sufficient method to estimate BMR. Next day weight prediction was also computed based on the estimated BMR. The results stated that the Harris Benedict equation produced more accurate predictions than the metabolic model proposed, therefore more work is necessary to find a model that accurately estimates BMR.