The vitality of reefs, as ecosystems in in jeopardy due to the impacts of climate change and human driven pollution and activities that call for immediate action through inventive restoration approaches. This research delves into exploring how cutting edge machine learning technologies like YOLOv8 and neural networks such, as RNNs and LSTMs can elevate the endeavors aimed at preserving reefs. We plan to use YOLOv8 to detect species such as crown of thorns starfish for real time monitoring and intervention purposes in the environment. Our strategy involves using RNNs to study time series data to predict health and species interactions based on environmental shifts. In addition to that approach; LSTM networks will contribute by capturing long term dependencies in coral reef data over time considering changes and climatic variations, for monitoring purposes. This comprehensive strategy aims to find locations for restoration projects and enhance how they are carried out while boosting the continuous monitoring of coral well being to support the durability and longevity of coral reef environments effectively in the long run. By delving into these approaches in research methods discussed here aims to offer knowledge that can guide decision making processes and enhance restoration tactics to facilitate a more efficient solution, to the current degradation of coral reefs.