This chapter explores the real-time applicability of the novel SB-class floaters by assessing their feasibility for sustainable marine applications. A case study was conducted to compare the application scenarios of the optimal bulbous-bottomed (SB-3) floater and the hemispherical (C-HS) floater in a WEC with a hydraulic power take-off system. Also, the feasibility of a WEC incorporating a long short-term memory (LSTM) algorithm for real-time wave condition monitoring was analyzed to forecast wave conditions in a diverse marine environment. The LSTM algorithm was trained using the floater’s hydrodynamic response and wave climate data. Subsequently, the trained LSTM model was employed to distinguish floater responses and wave data using a pre-defined threshold to differentiate calm and extreme wave conditions. The resulting LSTM-assisted WEC could be linked to an offshore sensor network via the Internet of Things (IoT), enabling self-sensing and self-powered applications. To be concise, this chapter examines whether the optimal SB-class floaters would be adequately advantageous if the conventional hemispherical floaters were replaced with them in commercially deployed WECs. Also, the energy balance was established to match the output power of an SB-3-assisted PA-WEC with the energy demands of the key components of a satellite-responsive buoy used for real-time navigation and sustainable oceanography.