Cloud computing has emerged as the primary method for storing and processing substantial volumes of digital data, such as images. The increase in cyberattacks, insider threats, and illicit surveillance has become data privacy and security critical concerns. AES, RSA, and ECC exemplify conventional encryption techniques that provide robust cryptographic guarantees. However, when utilized with cloud-based machine learning applications, they can provide challenges due to their complexity and inability to facilitate meaningful operations on encrypted data. Artificial Intelligence (AI) introduces innovative possibilities for encryption that are adaptable, efficient, and learning-based. These choices achieve a balance between security and usability in cloud environments. This study investigates the application of AI in image encryption for cloud storage, assessing traditional techniques, deep learning methodologies, and hybrid models that integrate homomorphic encryption with federated learning. We provide an experimental framework that assesses AES, perceptual encryption, and AI-driven algorithms according to characteristics such as entropy, correlation coefficient, NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity), and encryption speed. The findings indicate that AI-augmented encryption provides comparable security with reduced computational expense and facilitates limited analytics on the cloud side. Issues of scalability, adversarial robustness, and standardization are frequently discussed. The research indicates that AI-enhanced encryption is a viable method for enhancing the security, intelligence, and efficiency of cloud storage systems.