In our research, Calories burnt prediction, we aimed to utilize machine learning to predict calories burnt. The process involves processing the dataset, which contains information about a person. Like every machine learning work, we started with preparing the dataset. We performed exploratory Data Analysis. Moreover, we cleaned the dataset by finding and inputting missing values. Five machine learning models were used: KNN, Decision Tree, AdaBoost, SVM, and XGBoost. The dataset was trained on the above models. We trained and tested with default and hyperparameters to obtain the best result. Some of our models performed remarkably in predicting the calories burnt. We also used Explainable AI to find the link between provided features and our result. The research also highlights the importance of hyperparameters optimization for gaining the best output. All in all, this work demonstrates the introduction of advanced techniques like Machine learning in the physical exercise sector to predict calories loss based on personalized data, which can be further helpful for individual health and fitness programs. After experimenting with all the models using both default and optimized hyperparameter tuning we found that XGBoost provides the best result with RMSE 2.13 and R2 coefficient of 1.