The growing relevance of trust and privacy preservation in cloud computing environments stems from the need to preserve sensitive data, comply with regulations, and maintain user confidence in the face of evolving cyber risks and privacy issues. This study suggests a unique key strength assessment, trust model, and ABC-GOA hybrid optimization technique-based privacy-preserving mechanism. The trust model is essential for determining how trustworthy cloud service providers (CSPs) are. To assess the trustworthiness of CSPs, it considers elements including reputation, regulatory compliance, and user input. The trust model assists users in selecting CSPs for their requirements in data storage and processing by taking these factors into account. The suggested system includes key strength assessment, which uses Shannon entropy to assess the reliability of cryptographic keys, to improve data security. This assessment guarantees that the encryption keys used to safeguard sensitive data are robust enough to fend against assaults and illegal access. Users may be sure that their data is private and safe in the cloud environment by calculating the key strength. The hybrid ABC-GOA optimization approach optimizes the suggested mechanism's privacy and data security and this method combines the benefits of the two algorithms to improve the capabilities for exploration and exploitation. The ABC-GOA algorithm effectively explores the solution space and identifies the best solution, enhancing the privacy-preserving mechanism's overall functionality. To tackle an optimization challenge, our proposed model was compared to current models. The suggested model using the ABC-GOA algorithm has the greatest optimal cost value for privacy preservation, data security, and computational efficiency. This demonstrates the excellence and potency of our suggested technique in resolving the issues presented by data security and privacy in cloud systems.