The evolution of society has redefined people’s needs and expanded the possibilities available through technology to manage daily activities, including physical activity. The purpose of this research was to study the extent to which machine learning algorithms can contribute to the personalized suggestion of physical activity programs and the prediction of specific fitness goals. It is recognized that people’s daily lives are characterized by complex situations, such as the high prevalence of sedentary lifestyles, the methods of transport used in daily activities, attitudes toward physical activity, and more. Gaps in the literature focus on the lack of individualized recommendations for physical activity. It is further concluded that machine learning algorithms can model data governed by dynamic relationships, such as human behavior. The literature review shows that some machine learning algorithm models demonstrate high prediction accuracy, and that the choice of the appropriate algorithm is guided by the features given to the model.