The concept of motor prediction, first introduced by Helmholtz, involves the brain predicting the consequences of motor commands to control movements. This prediction is based on an efference copy, a copy of the motor command acting on the muscles, which helps in localizing visual objects and maintaining stable eye position. The idea has been extended to include the prediction of limb movements and complex interactions with the environment, such as tool use, through an internal forward model. This model simulates the dynamic behavior of the body and the environment, allowing for accurate control. Motor prediction is crucial for state estimation, where the brain estimates the body's state to ensure accurate control. Sensory information is often delayed and noisy, so combining sensory feedback with motor prediction can improve state estimation. For example, in object manipulation, predictive control can prevent slipping by adjusting grip force in response to load forces. Sensory confirmation and cancellation mechanisms further refine this process by filtering out irrelevant sensory information and attributing movements to self-generated or external sources. Context estimation is another key aspect, where the brain uses multiple forward models to predict and select appropriate controllers based on the current context. This modular approach helps in adapting to different environmental conditions and selecting the most suitable motor behavior. Additionally, motor prediction is essential for high-level cognitive functions such as action observation, mental practice, imitation, and social cognition, where the forward model predicts the sensory outcomes of actions without actually performing them. The article reviews the theoretical and experimental foundations of motor prediction, highlighting its importance in sensorimotor control and its applications in various cognitive domains.The concept of motor prediction, first introduced by Helmholtz, involves the brain predicting the consequences of motor commands to control movements. This prediction is based on an efference copy, a copy of the motor command acting on the muscles, which helps in localizing visual objects and maintaining stable eye position. The idea has been extended to include the prediction of limb movements and complex interactions with the environment, such as tool use, through an internal forward model. This model simulates the dynamic behavior of the body and the environment, allowing for accurate control. Motor prediction is crucial for state estimation, where the brain estimates the body's state to ensure accurate control. Sensory information is often delayed and noisy, so combining sensory feedback with motor prediction can improve state estimation. For example, in object manipulation, predictive control can prevent slipping by adjusting grip force in response to load forces. Sensory confirmation and cancellation mechanisms further refine this process by filtering out irrelevant sensory information and attributing movements to self-generated or external sources. Context estimation is another key aspect, where the brain uses multiple forward models to predict and select appropriate controllers based on the current context. This modular approach helps in adapting to different environmental conditions and selecting the most suitable motor behavior. Additionally, motor prediction is essential for high-level cognitive functions such as action observation, mental practice, imitation, and social cognition, where the forward model predicts the sensory outcomes of actions without actually performing them. The article reviews the theoretical and experimental foundations of motor prediction, highlighting its importance in sensorimotor control and its applications in various cognitive domains.