A finish-machining amount prediction apparatus configured to predict, from data of performing finish machining on respective parts of a component to be attached to a machine, finish-machining amounts of the respective parts of the component, the finish-machining amount prediction apparatus comprising: a machine learning device configured to learn the finish-machining amounts of the respective parts of the component in the finish machining, wherein the machine learning device has a state observation section configured to observe, as state variables expressing a current state of an environment, finish-machining amount data indicating the finish-machining amounts of the respective parts of the component in the finish machining and accuracy data indicating accuracy of respective parts of the machine, to which the component measured before a start of the finish machining is attached, a determination data acquisition section configured to acquire determination data indicating propriety determination results of the accuracy of the respective parts of the machine, to which the component after being subjected to the finish machining is attached, and a learning section configured to learn the finish-machining amounts of the respective parts of the component in the finish machining in association with the accuracy data by using the state variables and the determination data, wherein either the state observation section is further configured to observe type information for identifying a type of the component as one of the state variables, and the learning section is further configured to learn the finish-machining amounts of the respective parts of the component in the finish machining in association with both the accuracy data and the type information, and/or the learning section has an error calculation section configured to calculate an error between a correlation model that derives the finish-machining amounts of the respective parts of the component in the finish machining from the state variables and the determination data and a correlation feature identified from teacher data prepared in advance, and a model update section configured to update the correlation model to reduce the error. a machine learning device configured to learn the finish-machining amounts of the respective parts of the component in the finish machining, wherein the machine learning device has a state observation section configured to observe, as state variables expressing a current state of an environment, finish-machining amount data indicating the finish-machining amounts of the respective parts of the component in the finish machining and accuracy data indicating accuracy of respective parts of the machine, to which the component measured before a start of the finish machining is attached, a determination data acquisition section configured to acquire determination data indicating propriety determination results of the accuracy of the respective parts of the machine, to which the component after being subjected to the finish machining is attached, and a learning section configured to learn the finish-machining amounts of the respective parts of the component in the finish machining in association with the accuracy data by using the state variables and the determination data, wherein either the state observation section is further configured to observe type information for identifying a type of the component as one of the state variables, and the learning section is further configured to learn the finish-machining amounts of the respective parts of the component in the finish machining in association with both the accuracy data and the type information, the state observation section is further configured to observe type information for identifying a type of the component as one of the state variables, and the learning section is further configured to learn the finish-machining amounts of the respective parts of the component in the finish machining in association with both the accuracy data and the type information, and/or the learning section has an error calculation section configured to calculate an error between a correlation model that derives the finish-machining amounts of the respective parts of the component in the finish machining from the state variables and the determination data and a correlation feature identified from teacher data prepared in advance, and a model update section configured to update the correlation model to reduce the error. the learning section has an error calculation section configured to calculate an error between a correlation model that derives the finish-machining amounts of the respective parts of the component in the finish machining from the state variables and the determination data and a correlation feature identified from teacher data prepared in advance, and a model update section configured to update the correlation model to reduce the error.