The Industrial Internet of Things (IIoT) has revolutionized industrial operations with seamless connectivity and real-time decision-making, but the reliance on cloud computing poses significant security risks, such as data breaches and advanced persistent threats (APTs). Traditional anomaly detection methods are inadequate for the dynamic and complex IIoT-cloud ecosystem. This survey examines AI-based anomaly detection techniques, evaluating their performance in data confidentiality, real-time capabilities, scalability, and robustness against evolving cyber threats. Identified challenges include computational inefficiencies, scalability constraints, and insufficient privacy-preserving mechanisms. Based on these findings, future research directions include developing Federated Hybrid AI Frameworks that integrate Federated Learning, Reinforcement Learning, and Generative Adversarial Networks to address security and scalability challenges. Additionally, incorporating explainable AI techniques is crucial to enhance transparency and trust in detection systems. Aligning these efforts with stringent IIoT security demands is essential for building robust and adaptive frameworks for Industry 4.0.