Traditional deep learning models may struggle to capture all the relevant information and patterns due to the diverse and intricate nature of protein structures. By integrating multiple models or techniques, hybrid architectures aim to overcome these limitations and enhance prediction performance. In this chapter, we propose a novel hybrid deep learning architecture that combines the strengths of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance protein secondary structure prediction (PSSP) accuracy. Our approach integrates Inception modules with Bidirectional Gated Recurrent Units (BGRUs) and incorporates attention mechanisms to dynamically focus on the most relevant features within the protein sequences.