This thesis compares four machine learning classifiers for predicting coastal habitat types using drone-based multispectral imagery. Data from Akerøya Island was used to evaluate accuracy, training time, and prediction speed. Random Forest performed best, achieving around 89 percent accuracy with efficient processing. The results support the use of UAVs and Random Forest for scalable, high-resolution habitat mapping in coastal monitoring programs like SeaBee.