Emerging artificial intelligence (AI) and robotic surgical technologies have the potential to influence digital implant dentistry substantially. As a narrative review, and building on the foundations outlined in Part I, which described current digital tools and workflows alongside their persistent interface-related limitations, this second part examines how AI and robotics may overcome these barriers. This synthesis is based on peer-reviewed literature published between 2020 and 2025, identified through searches in PubMed, Scopus, and Web of Science. Current evidence suggests that AI-based approaches, including rule-based systems, traditional machine learning, and deep learning, may achieve expert-level performance in diagnostic imaging, multimodal data registration, virtual patient model generation, implant planning, prosthetic design, and digital smile design. These methods offer substantial improvements in efficiency, reproducibility, and accuracy while reducing reliance on manual data handling across software, datasets, and workflow interfaces. In parallel, robotic-assisted implant surgery has advanced from surgeon-guided systems to semi-autonomous and fully autonomous platforms, with the potential to provide enhanced surgical precision and reduce operator dependency compared with conventional static or dynamic navigation. Several of these technologies have already reached early stages of clinical deployment, although important challenges remain regarding interoperability, standardization, validation, and the continuing need for human oversight. Together, these innovations may enable the gradual convergence of digital technologies, real-time-assisted, unified, end-to-end implant prosthodontic workflows, and gradual automation, while acknowledging that full automation remains a longer-term prospect. By synthesizing current evidence and proof-of-concept applications, this review aims to provide clinicians with a comprehensive overview of the AI and robotics toolkit relevant to implant dentistry and to outline both the opportunities and remaining limitations of these disruptive technologies as the field progresses towards seamless, fully integrated treatment pathways.