Our current reliance on fossil fuels is the primary contributor to global warming and threatens our survival. Renewable energy is currently considered the leading solution to reduce greenhouse gas emissions. Energy extraction from the ocean tides (tidal turbines) can help fulfill the global renewable energy demand and combat world climate crises. Installations at this scale will have the associated benefit of reducing the levelised cost of tidal energy (LCOE) towards a target of EUR100/MWh, which will make ocean energy a viable option along with other renewable energy sources such as offshore wind. To achieve the target, increased performance and reliability of tidal energy devices are required. Tidal blades are a primary component of tidal turbines, possess a heterogeneous nature and can suffer from complex non-linear damage modes. For example, harsh marine environments can cause impact damage, delamination, matrix crack, fiber breakage or rupture, and others in tidal blades, which could lead to catastrophic failure of the system. Achieving reliable operational health and performance for the tidal blade is thus crucial for tidal energy companies. Fault diagnoses and maintenance operations are challenging in the sea; performance degradation, failure, or breakdown of the entire tidal energy system are more likely if unattended. Therefore, there is the need for real-time and reliable structure health monitoring (SHM) of tidal blades. The existing damage detection techniques have a limitation when dealing with the real-time environment, and do not take into account the along with uncertainty feature, we also addressed the issue of trusthworthiness in system decision employed with explanable artificial intelligence (XAI). This paper presents a real-time damage detection framework, information communication technologies (ICT) based infrastructure for real-time monitoring and proposes a novel model to classify/ detect the damages over blade structure. In addition a XAI based approach is proposed which based on supervised machine learning (ML) and uses an optimized convolutional neural network to classify from the heterogeneous data streams. Testing and evaluation of proposed approach in laboratory and operational settings is the future concern of this study.