Efficient detection in large-scale systems often requires balancing accuracy, computational cost, and memory management. This paper explores the integration of paging algorithms with particle swarm optimization (PSO), implemented via the PyCharm's library, to address detection tasks. By adopting paging-inspired strategies, candidate solutions are dynamically loaded and updated, reducing redundant computations and improving convergence efficiency in high-dimensional search spaces. Experimental results demonstrate that the paging–PSO hybrid framework enhances detection accuracy while lowering runtime overhead compared to standard PSO implementations. The proposed approach shows promise for resource-constrained environments, where optimization must be achieved under limited memory and processing capacity. This work highlights the potential of combining classical operating system principles with swarm intelligence, offering a novel pathway for efficient optimization in detection and classification problems.