Coral reef ecosystems are among the most diverse and significant marine habitats, yet increasingly threatened by climate change, pollution, and human activities. Accurate identification and classification of coral reefs are essential for monitoring their health and implementing conservation strategies. Traditional classification methods often struggle with underwater image distortions, low visibility, and complex reef structures. To address these challenges, this research presents a novel hybrid deep neural network framework that enhances coral reef identification using a private dataset. The study leverages the strengths of convolutional neural networks (CNNs) and bidirectional sequential models—Bi-GRU and Bi-LSTM—for learning temporal dependencies and contextual relationships within the data. Three hybrid architectures were explored: InceptionResNetV2 + Bi-GRU + Bi-LSTM, MobileNetV3Large + Bi-GRU + Bi-LSTM, and Xception + Bi-GRU + Bi-LSTM. Among these, the InceptionResNetV2-based model demonstrated the highest performance, achieving an accuracy of 94.43%. The results indicate that integrating CNNs with sequential learning models significantly enhances classification accuracy, minimizing loss classification of reef structures. This research contributes to marine ecosystem monitoring by providing an efficient, automated classification framework that can aid marine biologists and conservationists in tracking reef health over time. The proposed approach has the potential to be deployed in real-time coral monitoring systems, further improving the efficiency of large-scale reef assessments..