Machine learning (ML) is increasingly being applied to research on physical activity, sedentary behavior, and sleep. The complexity of human movement and non-movement behaviors makes traditional statistical methods inadequate, necessitating the use of ML for more accurate analysis. ML can handle complex, high-dimensional data and is suitable for tasks such as activity recognition, posture detection, and profile analysis. However, ML is not yet widely used in this field. This review introduces ML techniques to researchers in physical activity, sedentary behavior, and sleep research, explaining the ML modeling pipeline, supervised and unsupervised learning, and common algorithms. It highlights three research areas where ML has been successfully applied, emphasizing their successes and challenges. The review also discusses the practical aspects of ML, including tools for implementation and limitations of ML techniques. ML can help in identifying patterns of physical activity, classifying individuals based on sedentary behavior, and understanding relationships between sleep duration, variability, and quality. Supervised learning methods like decision trees, random forests, and support vector machines (SVM) are introduced, along with feature selection techniques and validation methods. Unsupervised learning methods such as K-means clustering and principal component analysis (PCA) are also discussed. Challenges such as overfitting, underfitting, and the curse of dimensionality are addressed, along with strategies to mitigate them. The review concludes that ML has significant potential for advancing research in physical activity, sedentary behavior, and sleep, but requires careful application and validation.Machine learning (ML) is increasingly being applied to research on physical activity, sedentary behavior, and sleep. The complexity of human movement and non-movement behaviors makes traditional statistical methods inadequate, necessitating the use of ML for more accurate analysis. ML can handle complex, high-dimensional data and is suitable for tasks such as activity recognition, posture detection, and profile analysis. However, ML is not yet widely used in this field. This review introduces ML techniques to researchers in physical activity, sedentary behavior, and sleep research, explaining the ML modeling pipeline, supervised and unsupervised learning, and common algorithms. It highlights three research areas where ML has been successfully applied, emphasizing their successes and challenges. The review also discusses the practical aspects of ML, including tools for implementation and limitations of ML techniques. ML can help in identifying patterns of physical activity, classifying individuals based on sedentary behavior, and understanding relationships between sleep duration, variability, and quality. Supervised learning methods like decision trees, random forests, and support vector machines (SVM) are introduced, along with feature selection techniques and validation methods. Unsupervised learning methods such as K-means clustering and principal component analysis (PCA) are also discussed. Challenges such as overfitting, underfitting, and the curse of dimensionality are addressed, along with strategies to mitigate them. The review concludes that ML has significant potential for advancing research in physical activity, sedentary behavior, and sleep, but requires careful application and validation.