In today’s fast-paced technological age, people tend to neglect the health and opt for unhealthy fast food due to busy schedules. This leads to consuming more calories, contributing to obesity. This paper gives a thorough analysis of five different machine learning algorithms—Linear Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost—to predict the number of human calories burned during physical activity. This study aims to evaluate the predictive Mean Absolute Error of different algorithms and pinpoint the most precise human calorie burn calculation model. To train and test the models on a diverse dataset including details on age, gender, amount of activity, heart rate, and other pertinent variables. Following feature engineering and preprocessing, we assessed the algorithms’ capacity to forecast calorie burn. The findings of this study have practical ramifications for several puroses, such as fitness observing, individualized health advice, and the creation of wearable technology. People can improve their general health, fitness, and well-being by making educated judgments about their physical activities and diet by precisely forecasting their calorie burn. Additionally, this work highlights the importance of choosing the right algorithms for certain predictive tasks and contributes to the body of knowledge in machine learning for wellness-related predictions.