In Industry 4.0, additive manufacturing (AM) is increasingly turning to artificial intelligence technology and in particular to the use of Machine Learning algorithms, given their beneficial contributions, enabling among other things, to improve the manufacturing process, offer AM designers a means of doing their studies before launching into manufacturing, help manufacturers categorize part quality and study the parameters that impact the processes used in manufacturing, etc. In this synthesis article, we present a study of the applicability of Machine Learning algorithms in the field of Additive Manufacturing, with the main aim of reviewing the main tasks to which these algorithms are applied: Processing Parameter Optimization, Property Prediction, Quality Prediction, Closed-Loop Control, Geometric Deviation Control, Defect Detection and the cost estimation. To achieve this, we have focused our research on articles published mainly Pin the last five years. In addition, this manuscript paves the way for future work on the applicability of ML to other processes, other materials that can be used in manufacturing and other tasks that are related to additive manufacturing.