This review explores the integration of artificial intelligence (AI) with nanomanufacturing, highlighting the potential of AI in enhancing nanomaterial synthesis, optimizing manufacturing processes, and enabling high-fidelity nanoscale characterization. The paper discusses various machine-learning and deep-learning algorithms used in analyzing nanoscale images, designing nanomaterials, and ensuring nano-quality assurance. It also outlines the challenges associated with applying machine- and deep-learning models for accurate predictions and explores the prospects of incorporating advanced AI algorithms such as reinforced learning, explainable AI (XAI), and big data analytics for material synthesis, manufacturing innovation, and nanosystem integration. Nanotechnology involves manipulating matter at the nanometer scale, with applications in medicine, electronics, and other fields. Nanomaterials, which range from nanoparticles to nanocomposites, are crucial in nanomanufacturing, which includes both top-down and bottom-up methods for fabricating nanoscale structures. Digital manufacturing, a key component of Industry 4.0, is increasingly being integrated with nanomanufacturing to improve efficiency and scalability. AI plays a vital role in nanomanufacturing, aiding in material synthesis, lithography, hotspot detection, spectroscopy, and characterization methods. The review also discusses the challenges in implementing AI in nanomanufacturing, including data availability, model understandability, computational complexity, security, and regulatory compliance. Despite these challenges, the integration of AI with nanomanufacturing holds significant promise for advancing nanotechnology and enabling more efficient and precise manufacturing processes.This review explores the integration of artificial intelligence (AI) with nanomanufacturing, highlighting the potential of AI in enhancing nanomaterial synthesis, optimizing manufacturing processes, and enabling high-fidelity nanoscale characterization. The paper discusses various machine-learning and deep-learning algorithms used in analyzing nanoscale images, designing nanomaterials, and ensuring nano-quality assurance. It also outlines the challenges associated with applying machine- and deep-learning models for accurate predictions and explores the prospects of incorporating advanced AI algorithms such as reinforced learning, explainable AI (XAI), and big data analytics for material synthesis, manufacturing innovation, and nanosystem integration. Nanotechnology involves manipulating matter at the nanometer scale, with applications in medicine, electronics, and other fields. Nanomaterials, which range from nanoparticles to nanocomposites, are crucial in nanomanufacturing, which includes both top-down and bottom-up methods for fabricating nanoscale structures. Digital manufacturing, a key component of Industry 4.0, is increasingly being integrated with nanomanufacturing to improve efficiency and scalability. AI plays a vital role in nanomanufacturing, aiding in material synthesis, lithography, hotspot detection, spectroscopy, and characterization methods. The review also discusses the challenges in implementing AI in nanomanufacturing, including data availability, model understandability, computational complexity, security, and regulatory compliance. Despite these challenges, the integration of AI with nanomanufacturing holds significant promise for advancing nanotechnology and enabling more efficient and precise manufacturing processes.