 This paper provides a comprehensive review of the literature published between 2015 and 2022 related to the use of machine learning algorithms for coastline slash shoreline extraction and change analysis, as well as coastal dynamic monitoring. The reviewed papers focus on the application of supervised and unsupervised learning algorithms such as support vector machines, SVM, random forest, RF, k-nearest neighbors, KNN, artificial neural networks, and deep learning, DL. The performance of these algorithms was evaluated by comparing the results with those obtained using traditional image processing techniques. Additionally, the advantages and disadvantages of each algorithm were discussed, along with the environmental data sets used in the studies. This article was authored by Crystal Volantis Antonio's Dietziakos and Christos Chakias.