While more and more technologies and software are being created and applied for the ocean setting, most of them still remain at high cost, and hinder the data to wider public. Understanding the marine biodiversity can be achieved through numerous ways, however, there is a lack of consensus and operability when depicting the marine megafauna population. Moreover, Deep Learning (DL) techniques are becoming accessible to wider population, and there is a potential of exposing them to the marine biologists, involving them to participate in public web-based dashboards, depicting those data. This dissertation addresses such issues, by providing an interactive dashboard, capable of fa cilitating the classification, prediction and deeper analysis of marine species. Using the State of Art (SoA) Machine Learning (ML) techniques for image vision, and providing the interactive vi sualizations, this thesis seeks to provide a less cumbersome apparatus for marine biologists, who can participate further in data gathering, labelling, depicting, ecological modelling, and potential calls for action. In further, this dissertation document provides the aquatic dashboard functionality using Human-Computer Interaction (HCI) techniques and interactive means to ease the upload, clas sification, and visualization of collected marine taxa, with a case study on marine megafauna imagery (e.g. whales, dolphins, sea birds, seals and turtles). As it will be hereinafter described, marine biologists, as end users, will evaluate of the proposed dashboard.