Marine soundscapes are complex and dynamic, with sounds generated by various sources. Machine learning has revolutionized the identification of these sources, but limited sample sizes remain a challenge. We addressed this issue by measuring sound continuously over long periods within marine areas designated under the Natura 2000 Network. Our approach combines event detection with artificial intelligence and statistical analysis to define novel smart-indices. This foundation enables further ecoacoustic analyses, including species or phenomenon identification in specific areas. Our study demonstrates the feasibility of real-time neural network classification for sound source recognition. Integrating this program into a single-board computer facilitates continuous monitoring, providing valuable insights into studied area behavior. Our approach paves the way for future research on source detection and location using sensor arrays. We have advanced our technology to support these complex approaches.