Cloud computing (CC) is a distributed architecture to enable accessing of several security measures. To ensure data privacy, a technique called homomorphic encoding is utilized to encode entities and retrieve data from the cloud server. However, there are challenges related to key management and allocation in homomorphic encoding, which can hinder the efficiency of the homomorphic encryption algorithm (HEA). This study addresses these issues by employing Particle Swarm Optimization (PSO) to generate inputs for the encoding process. PSO algorithms are nature-inspired meta-heuristic algorithms that mimic the collective behavior of birds and fishes to devise a computational approach. By leveraging the computational power of particles arranged in a specific pattern, the algorithm modifies the outcomes as particles explore the search space. A mathematical framework is employed to introduce randomness as the particles navigate the search area. By using an optimized PSO, a fixed number key is generated for performing the encoding process. MATLAB is executed for simulating the proposed homomorphic algorithm planned on the basis of Particle Swarm Optimization. The simulation demonstrates the efficiency of the suggested technique concerning resource usage and completion time. Unlike classic homomorphic algorithms, this approach offers greater flexibility in completion time and resource usage.