Numerous models and approaches have been researched and applied within the context of rapid advancements in information processing, robotics, automation, IoT, and AI applications. The next phase of this progression is anticipated to move beyond standalone AI models, with a growing emphasis on combining and utilizing multiple AI models to enhance accuracy, increase processing flexibility, and reduce costs. By synthesizing and examining methods for combining AI models, this paper categorizes and provides a comprehensive overview of the current state of collaborative AI, emphasizing effective multi-agent collaboration, communication, and system architecture design. This assessment identifies promising research directions that hold significant potential to contribute to the development and refinement of Collaborative AI, with particular focus on its applications in dynamic, real-world environments and its ability to address complex, multi-dimensional challenges.