In the rapidly evolving technological landscape, securing cloud computing environments while optimizing resource allocation is of paramount importance. This research study introduces a novel approach that seamlessly integrates deep learning with a nature-inspired optimization algorithm for achieving joint security and resource allocation. The proposed methodology harnesses the power of ResNet, a proven deep learning architecture, to bolster cloud security by identifying and mitigating threats effectively. Complementing this, the Flower Pollination Algorithm (FPA), inspired by natural pollination processes, is employed to strike an optimal balance between resource utilization and cost efficiency. This amalgamation creates a robust framework for managing cloud resources, ensuring the confidentiality, integrity, and availability of data and services, all while maintaining efficient resource allocation. The approach is flexible, adaptive, and capable of addressing the dynamic nature of cloud environments, making it a valuable asset for organizations seeking to enhance their cloud security posture without compromising on resource efficiency.