As cloud computing becomes ubiquitous, ensuring the privacy of sensitive data processed in cloud environments is of paramount importance. Organizations handling personal, financial, and healthcare data must adopt strategies that preserve data privacy while harnessing the power of artificial intelligence (AI). Privacy-preserving AI techniques such as federated learning, homomorphic encryption, and differential privacy offer promising solutions. This article explores these techniques in detail, their practical implementations, and the challenges they address, along with two real-world case studies. These methods enable organizations to build AI models on sensitive data while ensuring data confidentiality and regulatory compliance, opening the way for secure AI innovation in the cloud.