1. Passive Acoustic Monitoring is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches and indices are best suited for characterizing marine acoustic environments. 2. We present an alternative to the use of ecoacoustic indices and describe the application of multiple machine learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models, dimensionality reduction, and random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment. We processed two PAM databases and conducted 13 trials showing how acoustic features can be used to: i) discriminate between the vocalizations of marine mammals, beginning with high-level taxonomic groups, and extending to detecting differences between conspecifics belonging to distinct populations; ii) discriminating amongst different marine environments; and iii) detecting and monitoring anthropogenic and biological sound sources. 3. Acoustic features and their UMAP projections exhibited good performance in the classification of marine mammal vocalizations. Most of the taxonomic levels investigated here could be classified using the UMAP projections, apart from species that were underrepresented. Both anthropogenic (ships and airguns) and biological (humpback whales) sound sources could also be identified in field recordings. 4. We argue that acoustic feature extraction, visualization, and analysis allows the retention of most of the environmental information contained in PAM recordings, overcoming the limitations encountered when using ecoacoustics indices. Acoustic features are universal, permitting comparisons of results collected from multiple environments. Our approach can be used to simultaneously investigate the macro and micro characteristics of marine soundscapes, with a more objective method and with far less human effort.