Hybrid-ProtDeep presents a novel framework that integrates fixed-dimensional numerical representations, known as ProtPCV, with deep learning-based template search (TemPred) for enhanced protein structure prediction. This hybrid approach leverages the computational efficiency of ProtPCV and the predictive accuracy of TemPred, resulting in a significant improvement over traditional methods such as BLAST and PSI-BLAST, as well as recent deep learning techniques. By optimizing both speed and accuracy, Hybrid-ProtDeep demonstrates superior performance in identifying protein structures.