The design and use of cloud systems are being significantly altered by the confluence of artificial intelligence (AI) and the internet of things (IoT). Clouds can now process the massive amounts of data that are flowing in from networked devices in real time, rather than just storing static data and serving primarily as a cloud-based data warehouse. AI is used to identify patterns in the running sensor data and initiate certain actions in an ever-expanding IoT network. This enables the network to function with minimal or no human interaction, particularly when operating remotely or in real time. This chapter examines the specific factors causing this shift, with a focus on how AI and IoT are intertwined in modern cloud infrastructures. We discuss how they interact and provide examples of how they are used in a number of case studies, including those in industry, urban planning, agriculture, and healthcare. These examples demonstrate both the benefits and challenges of coping with increasingly interconnected and “smarter” systems. Concerns about trust, security, and privacy are prevalent in this change. Indeed, the variety of devices, the scalability of many devices, and the lack of device standards increase risks even if AI in cybersecurity and anomaly detection can be crucial for enhancing security suited to expanding IoT ecosystems. The consequences of compliance and ethical data practices are also covered in this chapter, especially as they relate to cloud space beyond national borders. Lastly, we examine the emerging potential of generative AI in enabling tasks such as automated edge behavior, the creation of synthetic data, and improvements in simulation modeling. To encourage the development of cloud ecosystems that are not only intelligent and adaptable, but also sustainable and morally sound, a forward-looking architectural model is offered as a guide.