Web applications are in their initial stages, where they have faced difficult challenges with security which the traditional defense mechanism continues to meet. This review examines in-depth the integration of full stack security with modern defense against sophisticated cyber threats using deep learning techniques in the entire application stack. 30% scholarly articles make up our entry point into the analysis of threat detection and prevention in certain applications using various deep learning architectures, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks to improve security for frontend interfaces, backend services and database systems. Our analysis shows a remarkable improvement in threat detection accuracy (mostly above 90%) in addition to an evident decrease in response times (30–76%) and a marked improvement in resource utilization efficiency (25–67%) compared to the standard rule-based approach. The most accurate human-AI collaboration models (99.3%), integrated learning techniques to collaborative defense that protects user privacy, and the growing usage of deployment tactics at the client end are some significant trends in this sector. Nonetheless, there are still certain issues with cross-platform compatibility, model clarity, and ongoing adaptability to changing possible threats. Therefore, in the increasingly complex threat environment, this article provides an overview of the present methods, new ideas, and possible future directions for using deep learning throughout the stack to additional online apps. Researchers, developers, and security experts are all served by this kind of examination.